{"id":175788,"url":"https://alion.io/job/cursor-software-engineer-rl-data","title":"Sr. Research Engineer, Synthetic Data","company":{"id":16873,"name":"Cursor","domain":"cursor.com","url":"https://alion.io/company/cursor","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":93,"open_postings":113,"ghost_share":0,"stale_share":0.088,"repost_share":0,"time_to_fill_p50_days":90,"computed_at":"2026-10-31T05:58:55Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Palo Alto, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":174000,"max_usd":388000,"period":"year","method":"role_country_seniority_unknown","sample_n":4425},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Post-training","optional":false},{"name":"Synthetic Data","optional":false}],"status":"live","first_seen_at":"2026-08-27T13:28:55Z","employer_posted_date":"2026-09-15","last_verified_at":"2026-10-31T15:51:45Z","board_verified":true,"closed_at":null,"days_open":65,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":64},"description":"SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars.\nSpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next.\nResearch Engineer, Synthetic Data\nAbout the Role\nAs a Sr. Research Engineer on the Synth Data team at SpaceXAI, you’ll invent and scale the synthetic data that teaches our models new capabilities. You own the full loop: spotting a capability the model is missing, designing a data generation strategy, building reliable pipelines and verifiers, and validating that the resulting data actually moves the model.\nYou form hypotheses about what data will help, build the systems to produce it at scale, run ablations, and read the resulting traces and training signals to understand why it worked (or didn’t). Your datasets go directly into frontier training runs, and shape the development of Grok models.\nWhat you’ll work on\nDesigning end-to-end synthetic data recipes that turn raw sources (traces, documents, codebases, workflows) into large, diverse, verifiable task sets.\n\nBuilding strong verification and anti-hacking mechanisms so rewards are simple to compute and hard to game.\n\nControlling difficulty and diversity so data stays learnable and reflects real usage instead of collapsing into templates.\n\nRunning targeted ablations and analyses that demonstrate a dataset caused a capability improvement (and ruling out grader or data artifacts).\n\nIterating from traces and training results to close specific gaps, then turning successful one-off recipes into reusable infrastructure.\n\nPartnering with research and other RL Data teams to translate model capability goals into concrete synthetic data plans.\n\nYou may be a fit if\nYou have research experience on synthetic or post-training data, in industry or academia. You've designed a data generation method, shipped the data into training, and can show its impact to the model you’re training.\n\nYou have strong software engineering fundamentals and have built non-trivial data pipelines, agent systems, or evaluation frameworks.\n\nYou have taste for what makes a good training task: hard enough to teach something real, simple enough to grade reliably, and resistant to reward hacking.\n\nYou are careful and systematic - understanding both aggregate metrics and close reading of raw outputs.\n\nYou enjoy turning messy, open-ended problems into repeatable, high-quality data production systems.\n\nApplying\nIf there appears to be a fit, we'll reach out to schedule 2-3 short technicals. 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