What are the responsibilities and job description for the Research Scientist – Frontier Data — AfterQuery position at David Joseph & Company?
Location: San Francisco, CA (Onsite)
Compensation: $150,000 – $250,000 base | $250,000 – $450,000 total cash (including profit sharing) competitive equity
Visa Sponsorship: None available
Employment Type: Full-Time
Headcount: 2 open seats
About AfterQuery
AfterQuery builds training data infrastructure and evaluation systems used by frontier AI labs to improve large language models and next-generation AI systems. The company is post-Series A with a small, high-impact technical team, direct partnerships with top frontier AI labs, and a focus on training data quality, evaluation rigor, and post-training optimization — with direct model improvement impact.
About The Role
This is a hands-on applied research role focused on model behavior, data quality, evaluation design, and RL training systems. This is not a pure academic research seat. This is not theoretical ML research. This is a fast-moving experimental execution role where you design experiments that directly improve frontier AI systems.
Core ownership includes:
Compensation: $150,000 – $250,000 base | $250,000 – $450,000 total cash (including profit sharing) competitive equity
Visa Sponsorship: None available
Employment Type: Full-Time
Headcount: 2 open seats
About AfterQuery
AfterQuery builds training data infrastructure and evaluation systems used by frontier AI labs to improve large language models and next-generation AI systems. The company is post-Series A with a small, high-impact technical team, direct partnerships with top frontier AI labs, and a focus on training data quality, evaluation rigor, and post-training optimization — with direct model improvement impact.
About The Role
This is a hands-on applied research role focused on model behavior, data quality, evaluation design, and RL training systems. This is not a pure academic research seat. This is not theoretical ML research. This is a fast-moving experimental execution role where you design experiments that directly improve frontier AI systems.
Core ownership includes:
- Designing high-signal datasets and identifying model failure modes
- Data slicing strategy, benchmark design, and evaluation framework construction
- Reward signal design, RLHF pipeline support, and RLVR experimentation
- Annotator behavior modeling and dataset diversity measurement
- Alignment capability measurement and quantitative experimentation
- Translating frontier lab training objectives into concrete datasets
- Fast experiment iteration under ambiguity across domains including finance, software engineering, enterprise workflows, and policy-related reasoning
- Strong quantitative research instincts with genuine curiosity about model behavior
- LLM training familiarity — RLHF understanding and RLVR familiarity
- Evaluation methodology fluency
- Research depth equivalent to a strong BS/MS researcher
- Ability to reason about how data structure changes model performance
- Lightweight experimentation ability and comfort with messy, incomplete data
- Cross-domain reasoning ability, fast iteration mindset, and builder mentality
- RL environment company experience (METR, Artificial Analysis, or similar)
- AI safety organization or benchmarking organization exposure
- Evaluation methodology work, dataset curation, or annotator/reward modeling
- Lab research experience and alignment research adjacency
- SWE research hybrid profile
- Pure theorists or PhD-only slow execution profiles
- Candidates who need clean specs or clean datasets to operate
- Profiles with low experimentation velocity
- $150,000 – $250,000 base salary
- Profit sharing — $250,000 – $450,000 total cash potential
- Competitive equity
- Bonus upside and founding team access
- Pending approval
- Initial screen
- Take-home
- Take-home review
- Onsite
- Offer
- Role is fully onsite in San Francisco — please only apply if you can commit to this
- No visa sponsorship available
Salary : $150,000 - $250,000