AfterQuery Experts Gigs

We're building a research cohort of senior domain experts across seven specialist tracks — software/systems engineering, formal methods and computational science, ML inference and GPU kernels, enterprise operations, security, hardware design, and creative technology — to help evaluate how frontier AI models reason about hard, real-world expert-level problems. This is research-and-evaluation work, not production engineering: you'll be defining what "correct" and "excellent" look like on problems you already know deeply. Candidates must fall into one of the profiles listed above to be considered. Tracks & Requirements: 1. Senior Software/Systems Engineer Domain: Software Engineering Experience: 8–12 years Education: Bachelor's (Master's preferred) 2. Research Scientist — Formal Methods / Computational Science Domain: Science (Math / Physics / Chemistry / Biology) Experience: 10+ years Education: PhD/Doctorate required 3. ML Research Engineer — Inference & GPU Kernels Domain: Machine Learning / AI Experience: 6–10 years Education: PhD/Doctorate, or Master's with a strong research record 4. Enterprise Operations / Domain Analyst Domain: Operations (Supply Chain / Finance / Compliance) Experience: 8–12 years Education: Bachelor's (professional certifications a plus) 5. Security Engineer — Cryptanalysis / Reverse Engineering Domain: Security Experience: 6–10 years Education: Bachelor's (Master's preferred) 6. Mechanical / Hardware Design Engineer Domain: Hardware (CAD / RTL / Robotics) Experience: 8–12 years Education: Bachelor's (PE license or Master's a plus) 7. Creative Technologist — Audio / Design / Linguistics Domain: Media Experience: 6–10 years Education: Bachelor's (Master's a plus) Candidates who meet the requirements and provide the requested materials will be prioritized for review. As part of this process, we conduct thorough background checks. Please apply only if you meet these requirements — candidates who meet fewer than 70% of the stated qualifications may be flagged for misrepresenting their professional experience, which could affect eligibility for future project staffing. Responsibilities: Design realistic technical scenarios and problem sets within your track Author expert-level reference solutions and grading rubrics Evaluate AI-generated outputs for correctness, depth, and domain judgment Qualifications: Meets the experience and education bar for at least one track in Full Description Currently or recently active in the field — hands-on, not purely academic-adjacent Strong written communication — you'll be authoring technical explanations and structured feedback, not just doing the work itself

We're assembling a group of senior software and ML engineers to work on one of the hardest open problems in AI today: how well can frontier models reason about LLM inference systems, GPU-level performance optimization, and model-serving architecture? You'll help design evaluation scenarios, write reference solutions, and grade model outputs on real systems-engineering problems — the same class of problems you likely work on day-to-day. This is a research-and-evaluation role, not a traditional engineering job — you won't be shipping production code for us, you'll be defining what "correct" and "excellent" look like for AI models tackling the problems you already know deeply. Responsibilities: What You'll Do Design realistic technical scenarios and problem sets in LLM inference optimization, GPU kernel design, and systems performance engineering Qualifications: 5–10 years of professional software engineering or ML systems experience Bachelor's degree or higher in Computer Science, Computer Engineering, Electrical Engineering, or a related field Strong, hands-on background in one or more of: GPU inference optimization, custom CUDA kernel development, model-serving systems (e.g., vLLM, TensorRT-LLM, SGLang), quantization, or optimizer design

This is a remote, project-based role for machine learning researchers with deep expertise in mechanistic interpretability. You will complete tasks at the frontier of interpretability research — including analyzing internal model representations, reverse-engineering learned circuits, and developing tools and techniques to understand how neural networks compute. Work is over the next 2–3 weeks, asynchronous, and assigned on a project-by-project basis, with an expected commitment of 10–20 hours per week for the projects you accept. This position offers exceptional pay, exposure to cutting-edge AI safety and interpretability research, and a strong addition to your research portfolio. Responsibilities: Conduct mechanistic interpretability research on transformer-based and other neural network architectures Identify, isolate, and analyze computational circuits responsible for specific model behaviors Apply and extend techniques such as activation patching, probing, sparse autoencoders, and attention analysis Develop tools and frameworks to automate or scale interpretability workflows across model families Qualifications: Published researcher with at least one first-author publication in a peer-reviewed venue (e.g., NeurIPS, ICML, ICLR, or equivalent) Master's or PhD in Machine Learning, Artificial Intelligence, Computer Science, or a related quantitative field Demonstrated expertise in mechanistic interpretability, model analysis, or AI safety research

This is a remote, project-based role for machine learning researchers and engineers with deep expertise in network science and graph-based modeling. You will complete tasks at the intersection of ML and network analysis — including model development, graph representation learning, and research tasks applied to real-world complex networks spanning social, biological, infrastructure, and information systems. Work is over the next 2–3 weeks, asynchronous, and assigned on a project-by-project basis, with an expected commitment of 10–20 hours per week for the projects you accept. This position offers exceptional pay, exposure to cutting-edge network ML research, and a strong addition to your research portfolio. Responsibilities: Apply machine learning techniques to complex network problems including community detection, link prediction, network generation, and dynamic network modeling Design and evaluate graph neural network architectures tailored to large-scale, heterogeneous, or temporal network data Develop generative and predictive models of network structure, diffusion processes, and cascading phenomena Conduct rigorous benchmarking of network ML models across diverse real-world graph datasets and tasks Qualifications: Published researcher with at least one first-author publication in a peer-reviewed venue (e.g., NeurIPS, ICML, ICLR, WWW, KDD, or equivalent) Master's or PhD in Computer Science, Applied Mathematics, Physics, Statistics, or a related quantitative field Demonstrated expertise in both machine learning and network science (e.g., graph theory, complex systems, network dynamics, or graph representation learning)

This is a remote, project-based role for PhD-level researchers with deep expertise in world models and generative AI. You will complete tasks at the frontier of world model research — including model development, evaluation, and research tasks spanning video prediction, environment simulation, planning, and learned latent world representations. Work is over the next 2–3 weeks, asynchronous, and assigned on a project-by-project basis, with an expected commitment of 10–20 hours per week for the projects you accept. This position offers exceptional pay, exposure to cutting-edge AI research problems, and a strong addition to your research portfolio. Responsibilities: Design, build, and evaluate world models for applications spanning video prediction, environment simulation, and agent planning Develop and experiment with latent space representations, dynamics models, and imagination-based planning approaches Conduct rigorous empirical evaluations of world model architectures across diverse environments and benchmarks Contribute to research directions in generative modeling, self-supervised learning, and model-based reinforcement learning Qualifications: PhD in Machine Learning, Artificial Intelligence, Computer Science, or a related quantitative field (or currently enrolled and ABD) Published researcher with at least one first-author publication in a peer-reviewed venue (e.g., NeurIPS, ICML, ICLR, CVPR, or equivalent) Demonstrated expertise in world models, generative modeling, or model-based reinforcement learning

This is a remote, project-based role for machine learning professionals with deep expertise in proteomics. You will complete tasks at the intersection of ML and proteomics — including model development, data analysis, and research tasks applied to real protein and mass spectrometry datasets. Work is over the next 2–3 weeks, asynchronous, and assigned on a project-by-project basis, with an expected commitment of 10–20 hours per week for the projects you accept. This position offers exceptional pay, exposure to cutting-edge biotech problems, and a strong addition to your research portfolio. Responsibilities: Apply machine learning techniques to proteomics data, including protein identification, quantification, and structure-function prediction tasks Build, train, and evaluate ML models tailored to mass spectrometry and proteomic datasets Develop predictive models using supervised and unsupervised learning approaches relevant to biological data Optimize model performance through feature engineering, hyperparameter tuning, and domain-specific preprocessing Qualifications: Published researcher with at least one first-author publication in a peer-reviewed journal Demonstrated expertise in both machine learning and proteomics (e.g., mass spectrometry data, protein databases, or related biological datasets) Strong problem-solving skills and ability to work independently on technical tasks

This is a remote, project-based role for machine learning researchers and engineers with deep expertise in ML applied to climate modeling and earth system science. You will complete tasks at the intersection of machine learning and climate systems — including model development, climate projection, parameterization schemes, and research tasks applied to long-term climate dynamics, carbon cycle modeling, and climate impact assessment. Work is over the next 2–3 weeks, asynchronous, and assigned on a project-by-project basis, with an expected commitment of 10–20 hours per week for the projects you accept. This position offers exceptional pay, exposure to cutting-edge climate ML research, and a strong addition to your research portfolio. Responsibilities: Apply machine learning techniques to climate modeling tasks including climate projection, parameterization emulation, bias correction, and climate impact modeling Build and evaluate ML models trained on earth system model outputs, observational records, and paleoclimate data Develop learned parameterization schemes for subgrid-scale processes such as convection, clouds, and ocean mixing Conduct rigorous evaluation of ML climate models against observational benchmarks and physics-based baselines Qualifications: Published researcher with at least one first-author publication in a peer-reviewed venue (e.g., NeurIPS, ICML, Nature Climate Change, Journal of Climate, Geophysical Research Letters, or equivalent) Master's or PhD in Climate Science, Earth System Science, Atmospheric Science, Applied Mathematics, Computer Science, or a related quantitative field Demonstrated expertise in both machine learning and climate modeling or earth system science

This is a remote, project-based role for machine learning researchers and engineers with deep expertise in ML applied to geologic modeling and earth science. You will complete tasks at the intersection of machine learning and geoscience — including model development, subsurface characterization, and research tasks applied to seismic interpretation, stratigraphic modeling, mineral exploration, and geophysical inversion. Work is over the next 2–3 weeks, asynchronous, and assigned on a project-by-project basis, with an expected commitment of 10–20 hours per week for the projects you accept. This position offers exceptional pay, exposure to cutting-edge geoscience ML research, and a strong addition to your research portfolio. Responsibilities: Apply machine learning techniques to geologic modeling tasks including seismic interpretation, facies classification, subsurface property prediction, and geophysical inversion Build and evaluate ML models trained on well log, seismic, remote sensing, and geochemical datasets Develop generative and predictive models for subsurface uncertainty quantification, stratigraphic forward modeling, and basin analysis Integrate ML approaches with physics-based geologic simulators and geostatistical workflows Qualifications: Published researcher with at least one first-author publication in a peer-reviewed venue (e.g., NeurIPS, ICML, Geophysics, Journal of Geophysical Research, Basin Research, or equivalent) Master's or PhD in Geoscience, Geophysics, Geology, Earth Science, Computer Science, or a related quantitative field Demonstrated expertise in both machine learning and geologic or geophysical modeling

This is a remote, project-based role for machine learning researchers and engineers with deep expertise in computational modeling of human and animal behavior. You will complete tasks at the intersection of ML and behavioral science — including model development, behavioral data analysis, and research tasks applied to decision-making, cognitive modeling, social dynamics, and agent-based simulations of behavior. Work is over the next 2–3 weeks, asynchronous, and assigned on a project-by-project basis, with an expected commitment of 10–20 hours per week for the projects you accept. This position offers exceptional pay, exposure to cutting-edge behavioral ML research, and a strong addition to your research portfolio. Responsibilities: Apply machine learning techniques to behavioral modeling tasks including decision-making inference, cognitive process modeling, social behavior prediction, and agent-based simulation Build and evaluate computational models of behavior using reinforcement learning, Bayesian inference, and deep learning approaches Develop and fit models to behavioral datasets from human experiments, animal studies, or large-scale observational data Conduct rigorous model comparison and validation against behavioral benchmarks and cognitive theory Qualifications: Published researcher with at least one first-author publication in a peer-reviewed venue (e.g., NeurIPS, ICML, Psychological Review, Cognitive Science, PNAS, or equivalent) Master's or PhD in Computational Neuroscience, Cognitive Science, Psychology, Computer Science, or a related quantitative field Demonstrated expertise in both machine learning and computational behavioral modeling (e.g., reinforcement learning models of behavior, Bayesian cognitive models, or social simulation)

This is a remote, project-based role for machine learning researchers and engineers with deep expertise in natural language processing applied to financial data and systems. You will complete tasks at the intersection of NLP and finance — including model development, fine-tuning, and research tasks applied to earnings calls, financial filings, market sentiment, news analytics, and other text-rich financial data sources. Work is over the next 2–3 weeks, asynchronous, and assigned on a project-by-project basis, with an expected commitment of 10–20 hours per week for the projects you accept. This position offers exceptional pay, exposure to cutting-edge finance NLP research, and a strong addition to your research portfolio. Responsibilities: Apply NLP and large language model techniques to financial data including SEC filings, earnings transcripts, analyst reports, and financial news Build and fine-tune models for financial sentiment analysis, event detection, named entity recognition, and information extraction Develop predictive models linking textual signals to market movements, credit risk, or other financial outcomes Design and evaluate retrieval-augmented generation (RAG) pipelines for financial document question answering and summarization Qualifications: Published researcher with at least one first-author publication in a peer-reviewed venue (e.g., ACL, EMNLP, NAACL, NeurIPS, or equivalent) Master's or PhD in Computer Science, Computational Linguistics, Statistics, Finance, or a related quantitative field Demonstrated expertise in NLP and its application to financial data, markets, or economic text corpora

This is a remote, project-based role for machine learning researchers and engineers with deep, specialized expertise in diffusion models. You will complete tasks at the frontier of diffusion-based generative AI — including model development, architectural experimentation, fine-tuning, and research tasks spanning image, video, audio, and scientific data generation. Work is over the next 2–3 weeks, asynchronous, and assigned on a project-by-project basis, with an expected commitment of 10–20 hours per week for the projects you accept. This position offers exceptional pay, exposure to cutting-edge generative AI research, and a strong addition to your research portfolio. Responsibilities: Design, implement, and evaluate diffusion model architectures including DDPM, score-based, flow matching, and latent diffusion approaches Develop and experiment with novel sampling strategies, noise schedules, and guidance techniques to improve generation quality and efficiency Fine-tune and adapt pre-trained diffusion models for specific domains, modalities, and downstream tasks Conduct rigorous benchmarking of diffusion models across perceptual quality, diversity, and controllability metrics Qualifications: Published researcher with at least one first-author publication in a peer-reviewed venue (e.g., NeurIPS, ICML, ICLR, CVPR, or equivalent) Master's or PhD in Machine Learning, Computer Science, Statistics, or a related quantitative field Deep, demonstrated expertise in diffusion models, score-based generative models, or flow-based approaches

This is a remote, project-based role for machine learning researchers and engineers with deep expertise in ML applied to chip design and electronic design automation (EDA). You will complete tasks at the intersection of machine learning and VLSI design — including model development, optimization, and research tasks applied to placement, routing, synthesis, verification, and other EDA workflows. Work is over the next 2–3 weeks, asynchronous, and assigned on a project-by-project basis, with an expected commitment of 10–20 hours per week for the projects you accept. This position offers exceptional pay, exposure to cutting-edge ML for hardware research, and a strong addition to your research portfolio. Responsibilities: Apply machine learning techniques to chip design workflows including placement, routing, floorplanning, synthesis, and timing analysis Build and evaluate ML models for design space exploration, performance prediction, and design rule verification Develop reinforcement learning, graph neural network, or generative modeling approaches tailored to EDA applications Collaborate on integrating ML components into existing EDA pipelines and design flows Qualifications: Published researcher with at least one first-author publication in a peer-reviewed venue (e.g., DAC, ICCAD, NeurIPS, ICML, or equivalent) Master's or PhD in Electrical Engineering, Computer Engineering, Computer Science, or a related quantitative field Demonstrated expertise in both machine learning and chip design or EDA (e.g., VLSI, RTL design, physical design, or verification)

This is a remote, project-based role for machine learning researchers and engineers with deep expertise in image generation and generative modeling. You will complete tasks at the frontier of generative AI — including model development, fine-tuning, evaluation, and research tasks applied to diffusion models, GANs, autoregressive image models, and related architectures. Work is over the next 2–3 weeks, asynchronous, and assigned on a project-by-project basis, with an expected commitment of 10–20 hours per week for the projects you accept. This position offers exceptional pay, exposure to cutting-edge generative AI research, and a strong addition to your research portfolio. Responsibilities: Design, build, and evaluate state-of-the-art image generation models including diffusion models, GANs, VAEs, and autoregressive approaches Fine-tune and adapt pre-trained generative models for specific domains, styles, and downstream applications Conduct rigorous evaluation of image generation quality using perceptual, statistical, and task-specific metrics Experiment with conditioning mechanisms, guidance techniques, and controllable generation approaches Qualifications: Published researcher with at least one first-author publication in a peer-reviewed venue (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, or equivalent) Master's or PhD in Machine Learning, Computer Vision, Computer Science, or a related quantitative field Demonstrated expertise in image generation, generative modeling, or closely related areas of deep learning

This is a remote, project-based role for machine learning researchers and engineers with deep expertise in ML applied to geologic modeling and earth science. You will complete tasks at the intersection of machine learning and geoscience — including model development, subsurface characterization, and research tasks applied to seismic interpretation, stratigraphic modeling, mineral exploration, and geophysical inversion. Work is over the next 2–3 weeks, asynchronous, and assigned on a project-by-project basis, with an expected commitment of 10–20 hours per week for the projects you accept. This position offers exceptional pay, exposure to cutting-edge geoscience ML research, and a strong addition to your research portfolio. Responsibilities: Apply machine learning techniques to geologic modeling tasks including seismic interpretation, facies classification, subsurface property prediction, and geophysical inversion Build and evaluate ML models trained on well log, seismic, remote sensing, and geochemical datasets Develop generative and predictive models for subsurface uncertainty quantification, stratigraphic forward modeling, and basin analysis Integrate ML approaches with physics-based geologic simulators and geostatistical workflows Qualifications: Published researcher with at least one first-author publication in a peer-reviewed venue (e.g., NeurIPS, ICML, Geophysics, Journal of Geophysical Research, Basin Research, or equivalent) Master's or PhD in Geoscience, Geophysics, Geology, Earth Science, Computer Science, or a related quantitative field Demonstrated expertise in both machine learning and geologic or geophysical modeling

This is a remote, project-based role for machine learning professionals with deep expertise in bioinformatics. You will complete tasks at the intersection of ML and biological data analysis — including model development, pipeline construction, and research tasks applied to real genomic, transcriptomic, or multi-omics datasets. Work is over the next 2–3 weeks, asynchronous, and assigned on a project-by-project basis, with an expected commitment of 10–20 hours per week for the projects you accept. This position offers exceptional pay, exposure to cutting-edge biotech problems, and a strong addition to your research portfolio. Responsibilities: Apply machine learning techniques to biological datasets, including genomic, transcriptomic, proteomic, or multi-omics data Build, train, and evaluate ML models tailored to bioinformatics applications such as variant calling, gene expression analysis, or pathway modeling Develop predictive models using supervised and unsupervised learning approaches relevant to biological systems Design and optimize bioinformatics pipelines integrating ML components for scalable, reproducible analysis Qualifications: Published researcher with at least one first-author publication in a peer-reviewed journal Demonstrated expertise in both machine learning and bioinformatics (e.g., sequence analysis, genomics, transcriptomics, or related biological data domains) Strong problem-solving skills and ability to work independently on technical tasks

This is a remote, project-based role for machine learning researchers and engineers with deep expertise in neuromorphic computing and brain-inspired AI systems. You will complete tasks at the intersection of ML and neuromorphic hardware — including spiking neural network development, hardware-aware model design, and research tasks applied to energy-efficient, event-driven computing architectures. Work is over the next 2–3 weeks, asynchronous, and assigned on a project-by-project basis, with an expected commitment of 10–20 hours per week for the projects you accept. This position offers exceptional pay, exposure to cutting-edge neuromorphic AI research, and a strong addition to your research portfolio. Responsibilities: Design, implement, and evaluate spiking neural networks (SNNs) and other brain-inspired computing models for real-world tasks Develop hardware-aware ML approaches optimized for neuromorphic chips and event-driven architectures Apply surrogate gradient methods, spike timing-dependent plasticity (STDP), and other biologically inspired learning rules Benchmark neuromorphic models against conventional deep learning baselines across accuracy, latency, and energy efficiency metrics Qualifications: Published researcher with at least one first-author publication in a peer-reviewed venue (e.g., NeurIPS, ICML, ICLR, ISSCC, or equivalent) Master's or PhD in Computer Science, Electrical Engineering, Computational Neuroscience, or a related quantitative field Demonstrated expertise in neuromorphic computing, spiking neural networks, or brain-inspired AI architectures

This is a remote, project-based role for machine learning researchers and engineers with deep expertise in ML applied to atmospheric modeling and climate science. You will complete tasks at the intersection of machine learning and atmospheric systems — including model development, data assimilation, and research tasks applied to weather prediction, climate projection, extreme event detection, and atmospheric dynamics. Work is over the next 2–3 weeks, asynchronous, and assigned on a project-by-project basis, with an expected commitment of 10–20 hours per week for the projects you accept. This position offers exceptional pay, exposure to cutting-edge climate and weather ML research, and a strong addition to your research portfolio. Responsibilities: Apply machine learning techniques to atmospheric modeling tasks including weather forecasting, climate downscaling, extreme event detection, and atmospheric state estimation Build and evaluate ML models trained on reanalysis, observational, and simulation data from atmospheric and climate systems Develop hybrid modeling approaches that integrate physical constraints and numerical weather prediction outputs with data-driven methods Conduct rigorous benchmarking of ML models against operational weather and climate baselines Qualifications: Published researcher with at least one first-author publication in a peer-reviewed venue (e.g., NeurIPS, ICML, Journal of Climate, Geophysical Research Letters, or equivalent) Master's or PhD in Atmospheric Science, Meteorology, Climate Science, Applied Mathematics, Computer Science, or a related quantitative field Demonstrated expertise in both machine learning and atmospheric or climate modeling

This is a remote, project-based role for data scientists and machine learning professionals with 1–3 years of work experience. You will complete tasks similar to those performed in data science, machine learning engineering, AI research, or analytics roles. Work is over the next 2-3 weeks, asynchronous, and assigned on a project-by-project basis, with an expected commitment of 10–20 hours per week for the projects you accept. This position offers highly competitive pay, exposure to real-world ML/data problems, and an excellent opportunity to boost your resume. Responsibilities: Assist in building, training, and evaluating machine learning models for various business applications Develop predictive models using supervised and unsupervised learning techniques Optimize model performance through hyperparameter tuning and feature engineering Document methodologies, model assumptions, and technical approaches Qualifications: 1–3 years of professional experience in data science, machine learning, or related fields Strong problem-solving skills and ability to work independently on technical tasks Masters degree in Computer Science, Statistics, Mathematics, Data Science, or related quantitative field

We are seeking elite Content Writers & AI Evaluation Specialists with expertise in copywriting, content creation and LLM evaluation. We are seeking elite Content Writers & AI Evaluation Specialists with expertise in copywriting, content creation and LLM evaluation. In this role, you will apply your deep expertise in writing, content strategy, and AI evaluation to create, review, and evaluate creative and informative text formats, including stories, articles, summaries, dialogues based on guidelines while ensuring quality, accuracy, and alignment with user intent. Note: This is an Independent Contractor position that requires a 40-hour per week commitment for the next 3–6 months. You will be working directly with one of our partners. Responsibilities: Create and refine comprehensive prompts, creative content, and explanatory materials within your domain of expertise Write, rewrite, and edit content for various AI training scenarios, including crafting high-quality prompts and responses to test and improve model capabilities Conduct in-depth evaluations of AI-generated content for fluency, coherence, factuality, creativity, and bias detection Offer structured feedback to improve the AI's understanding of complex content creation, nuanced language use, and domain-specific writing principles Qualifications: Bachelor's degree in English, Creative Writing, Linguistics, Computer Science, or related field Strong understanding of LLM capabilities and limitations Excellent writing and editing skills, with the ability to adapt to different styles and tones