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: 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 Experience at a recognized technology or AI company, in a role with real systems-engineering ownership

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 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