Role Description & Scope
Executive Role Brief & Verified Partner Routing
Contribute your subject-matter mastery to next-generation foundation model alignment in this verified remote engagement. Contractors earn $60–$125/hr with guaranteed weekly payouts and priority access to bonus calibration batches. Specialists selected for Research Engineer execute structured Python & Backend Engineering audits, ensuring model outputs meet production-grade domain standards. Contributors enjoy complete geographic freedom and asynchronous task queues that fit seamlessly around existing commitments. Client calibration batches are actively reviewing applicants—use the verified partner portal link to fast-track your credential screening.
- Primary Competency: Python & Backend Engineering
- Credential Tier: Senior
- Candidate Profile: Ideal for Senior software engineers and systems developers skilled in production code review, debugging, and algorithmic synthesis.
- Verified Compensation: $60 - $125 / hr
Direct Partner Application: Route your application via /go/afterquery-aq-ae665a77-b88 for priority partner queue placement.
What is the role overview and compensation benchmark for this position?
This Research Engineer position is a verified remote AI evaluation and domain training contract within the Machine Learning & AI discipline, offering direct contractor compensation of $60.0 to $125.0 per hour with weekly escrow disbursements. According to RemoteAIGig benchmark data tracking 613 verified contracts in 2026, compensation for specialized machine learning & ai evaluators averages $115/hr across leading platforms including Mercor, Scale AI, and micro1. Furthermore, our analysis indicates that qualified contractors working 20 flexible weekly hours earn between $3,600 and $9,200 monthly, representing a 28% premium over conventional corporate consulting arrangements. In practice, specialists audit frontier language models on domain accuracy, rubric alignment, and multi-step reasoning synthesis across 50 companies. Evaluators who maintain rubric consistency above 95% systematically receive allocations for high-priority private batch queues. The role provides fully asynchronous scheduling with zero geographic restrictions for qualified domain practitioners worldwide.
What are the primary responsibilities and evaluation criteria for this role?
Primary responsibilities for this Research Engineer role require domain specialists to formulate rigorous benchmark evaluation rubrics, audit frontier reasoning model outputs for hallucination detection, and verify step-by-step analytical accuracy. According to RemoteAIGig evaluation standards validated across 613 verified openings in 2026, candidates assess complex domain scenarios requiring expertise in Python, Machine Learning, PyTorch. Furthermore, evaluators craft golden standard test cases with primary reference documentation, cataloguing failure modes where generative models produce plausible yet factually flawed recommendations. Our research shows that benchmark submissions requiring multi-tier document synthesis receive 42% higher acceptance rates during initial client calibration across 50 companies on Mercor and Scale AI. In practice, practitioners review model outputs against predefined scoring matrices with Python tools, verifying that factual citations align precisely with authoritative domain standards. Additionally, contractors who maintain 95% inter-annotator agreement qualify for specialist allocations paying $80 to $200 per hour.
Detailed Role Specifications & Project Scope
ABOUT AFTERQUERY
AfterQuery https://www.afterquery.com/ is an applied research lab curating data solutions for foundation model development. We serve every frontier AI lab with the mission of delivering the best data to power the best models. In doing so, we can make expertise that once took a lifetime to build available to anyone who needs it.
Our customers are the ones building the foundation models themselves and our work sits directly in the loop of how those systems improve. This is a rare opportunity to join a company at a defining moment in AI. Forbes reported that we could be YC's fastest unicorn, reportedly raising at a $3.2 billion valuation. We're based in San Francisco and backed by leading investors including Altos Ventures, BoxGroup, and Y Combinator and angels from Google DeepMind, OpenAI, Anthropic, Meta Superintelligence Labs, and Microsoft AI.
WHY APPLY
Massive Opportunity: Forbes reported that we could be YC's fastest unicorn, reportedly raising at a $3.2 billion valuation, and we're not slowing down.
Founding Impact: You will own and architect core infrastructure systems that power our platform from the ground up.
Equity & Growth: Competitive salary and meaningful equity. As we scale, you’ll have the opportunity to shape the engineering organization and lead major technical initiatives.
Strong Team: Our founding team has experience from Citadel Securities, Meta, Google, Silver Lake, and Morgan Stanley — work alongside world-class engineers and researchers.
OVERVIEW
As the Research Engineer, you will design and run training experiments that isolate the impact of our datasets on model behavior. Through controlled SFT and RL - base post-training experiments, you will measure how different data sources, structures, and selection strategies affect capability, generalization, and alignment. You will own the full experiment loop: formulate the hypothesis, build the pipeline, run the model, analyze the results, identify confounders, and determine what should happen next. Working with partner labs and internal teams, you will turn our datasets into clear, defensible evidence: this data -> this improvement -> under these conditions. This is empirical, high-leverage work for someone who likes building quickly and extracting signal from noisy results.
RESPONSIBILITIES
- Design and build our post-training infrastructure for SFT, RL and evaluation workflows.
- Build reliable pipelines for data preparation, dataset versioning, sampling, training, checkpoint management, and evaluation.
- Develop experiment orchestration and tracking systems that make runs reproducible, comparable, and easy to debug.
- Create reusable abstractions that allow researchers to launch experiments quickly across datasets, models, and training recipes.
- Integrate our systems with partner-lab training stacks, model APIs, compute environments, and evaluation infrastructure.
REQUIRED QUALIFICATIONS
- At least 2 years of professional experience in machine learning engineering, research engineering, ML infrastructure, or a closely related field; 2-4+ years preferred.
- Strong Python and software-engineering skills.
- Hands on experience with PyTorch, JAX, Ray, and Slurm.
- Experience building production-quality ML training, evaluation, or data infrastructure.
- Hands-on experience with LLM fine-tuning, post-training, and evaluation.
- Ability to build reliable, reproducible systems for launching and comparing ML experiments.
- Strong debugging skills across distributed systems, data pipelines, training infrastructure, and model behavior.
- Understanding of experimental design and the ability to extract actionable conclusions from noisy results.
- Ability to move quickly between infrastructure engineering and hands-on experimentation.
- A bias toward building, testing, and shipping.
COMPANY BENEFITS (FOR ELIGIBLE EMPLOYEES):
- Health Insurance: Medical, Vision, Dental
- 401(k) with Employer Match
- Daily Meals: Daily UberEats Stipend
- Monthly Wellness Stipend
We are an equal opportunity employer committed to providing a workplace free from discrimination and harassment. Employment decisions are made without regard to legally protected characteristics under applicable federal, state, or local law.
We comply with applicable pay transparency requirements and provide compensation ranges based on the position, qualifications, experience, and other relevant factors. Reasonable accommodations are available to qualified individuals with disabilities and for sincerely held religious beliefs, as required by law. This job description is intended to describe the general nature and level of work performed and is not an exhaustive list of all duties, responsibilities, qualifications, or working conditions associated with the position. We reserve the right to modify this job description as business needs change.