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. Compensation is benchmarked at $60–$125/hr, reflecting top-tier market rates for specialized AI evaluation work. In this Research Scientist - Post Training workstream, practitioners apply RLHF & LLM Rubric Evaluation to stress-test frontier model outputs against strict scoring rubrics. Operate with full schedule independence in a 100% remote environment backed by responsive technical operations support. Client calibration batches are actively reviewing applicants—use the verified partner portal link to fast-track your credential screening.
- Primary Competency: RLHF & LLM Rubric Evaluation
- Credential Tier: PhD
- Candidate Profile: Ideal for PhD analytical evaluators experienced in structured rubric scoring, prompt stress-testing, and hallucination auditing.
- Verified Compensation: $60 - $125 / hr
Direct Partner Application: Route your application via /go/afterquery-aq-41a7fe68-689 for priority partner queue placement.
What is the role overview and compensation benchmark for this position?
This Research Scientist - Post Training 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 Scientist - Post Training 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 LLM. 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
Your job is to prove that our data works. You will design and run training experiments that isolate the impact of our datasets on model behavior. This includes SFT and RL-based post-training, where you’ll measure how different data sources shift capability, generalization, and alignment. Working closely with partner labs, you will turn our datasets into clear, defensible evidence: this data → this improvement → under these conditions. This is experimental, high-leverage work.
RESPONSIBILITIES
- Run controlled SFT and RL experiments to measure the impact of our datasets on model performance.
- Help build public evals and new data types that push the frontier.
- Publish external-facing research, blog posts, and technical reports.
- Work with internal SPLs to iterate on data quality based on your results.
REQUIRED QUALIFICATIONS
- Strong familiarity with LLM training and evaluation methodologies.
- Ability to design lightweight experiments, move fast, and extract actionable insights from messy results.
- Comfort working across domains (you'll touch finance, software engineering, policy, and more).
- A bias toward building over theorizing.
PREFERRED QUALIFICATIONS
- Great candidates are undergrad research or master's research (but haven't done a phd).
- Genuine obsession with how data structure, selection, and quality drive model behavior.
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.