AfterQuery — Software Engineer, RL Environments

Type: Full-time | On-site | San Francisco, CA Compensation: $180,000–$220,000 + competitive equity (see comp note below) Hiring count: 3 Visa sponsorship: O-1, OPT Reports to: Not specified (initial screen: Alec; second round: Michael/Sam)

About AfterQuery

AfterQuery builds the training data and evaluation infrastructure that frontier AI labs use to improve their models, designing high-signal datasets and running rigorous evaluations that go beyond static benchmarks. It's a small, early team (post–Series A) where individual contributors have direct impact on how the next generation of models learns. The founding team comes from Jane Street, Citadel, Google, Goldman, and Stanford AI Lab.

Founded: 2025 | Team size: 11–50 | Total funding: $30M raised (~$300M valuation) Industry: Consumer Tech Website: afterquery.com Office: San Francisco, CA

Why Candidates Should Join

Outsized total cash: $200K base plus profit share of roughly 150% of base, bringing expected total cash to around $500K, plus competitive equity.
Direct line to frontier models: Output feeds directly into model training runs at scale, working hands-on with research teams at top AI labs.
High ownership, early team: Small post–Series A team where ICs scope, build, and ship end to end.

The Role

As a SWE (Environments), you design the datasets and evaluation rubrics that directly influence how frontier models learn — going from hypothesis to live experiment quickly, with output feeding directly into model training.

What You'll Be Doing

Design data slices and explore data shapes that expose meaningful model failure modes across domains like finance, code, and enterprise workflows
Build and refine evaluation rubrics and reward signals for RLHF and RLVR training pipelines
Model annotator behavior and run experiments to improve different model capabilities
Develop quantitative frameworks for measuring dataset quality, diversity, and downstream impact on model alignment and capability
Create and manage both real-world and synthetic data pipelines
Partner with lab research teams to translate their training objectives into concrete data and evaluation specifications

Tech stack: Not specified

Requirements

1–4 years of software engineering experience with strong technical depth
Design targeted data slices that surface model failure modes across high-stakes domains (finance, code generation, enterprise workflows)
Build and iterate on evaluation rubrics and reward signals powering RLHF and RLVR training pipelines
Develop quantitative frameworks to measure dataset quality, diversity, and downstream impact on model alignment and capability
Own end-to-end real world and synthetic data pipelines, from scoping with research teams to production-ready evaluation specs
Run annotator modeling experiments to improve model capabilities across task types

Green Flags

Experience at RL environment companies
Background in AI safety or benchmarking organizations like METR or Artificial Analysis
Genuine obsession with how data structure, selection, and quality drive model behavior
Ability to design lightweight experiments and move fast
Former founders or early engineers at early stage startups
Demonstrated ability to work hard, learn fast, and care deeply about details

Red Flags

Pure research profile with limited engineering output, this is a SWE role, shipping matters
Looking for standard product engineering work — the real scope is data pipelines, reward modeling, and eval infra

Role Details

Salary$180,000–$220,000Comp note$200K base + ~150% profit share (~$500K total cash) + competitive equityEquityCompetitive equityOn-site policyOn-site, San Francisco, CAVisa sponsorshipO-1, OPTEmployment typeFull-timeLocationSan Francisco, CAHiring count3

Screening Questions

(Required Candidate Q&A on the Contrario submission form)

LinkedIn Profile
Are you legally authorized to work in the country where you are applying?
Will you now or in the future require visa sponsorship for employment?

Interview Process

Stage 1 — Pending Approval — Candidates awaiting initial approval. Stage 2 — Initial Screen (Alec) Stage 3 — Second Round (Michael/Sam) Stage 4 — Take Home Stage 5 — Post Take Home Stage 6 — Work Trial Stage 7 — References Stage 8 — Offer Extended Stage 9 — Candidate Hired — Candidate accepts and starts.

Ideal Companies & Backgrounds

Deeptune, Preference Model, Matrices, Mechanize, Proximal, Habitat Inc, Phinity Labs, OpenReward, Veris AI, Steadyworks, Sepal AI, Trajectory, Applied Compute, CGFT, OpenPipe, Isidor, VMAX

(17 companies shown; page appeared fully loaded — no "Show all" collapse detected.)

Notes

Comp: Structured Job Details lists $180K–$220K; role body describes $200K base + ~150% profit share (~$500K total cash). Band above follows the structured field; total-cash figure retained as a comp note.
Sourced from the outreach template (lowest-priority source, not contradicted by any higher section, so carried in): $30M raised (~$300M valuation) and founding-team pedigree (Jane Street, Citadel, Google, Goldman, Stanford AI Lab). Worth a quick confirm with Contrario if leaned on heavily in candidate-facing materials.
Must-Have (role body, for reference): genuine obsession with how data quality drives model behavior; ability to design lightweight experiments and extract insight from messy results; comfort across domains (finance, SWE, policy); track record of shipping / bias toward building.

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