{"id":3882865,"url":"https://alion.io/job/clera-lead-research-engineer-data-quality-14","title":"Lead Research Engineer, Data Quality","company":{"id":2706,"name":"Clera","domain":"getclera.com","url":"https://alion.io/company/clera","size_band":"11-50","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":80,"open_postings":39,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":6,"computed_at":"2026-11-09T06:03:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":130000,"max":225000,"currency":"USD","period":"year","gross":null,"usd_annual":225000},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Docker","optional":false},{"name":"Linux","optional":false},{"name":"Post-training","optional":false},{"name":"Python","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Synthetic Data","optional":false}],"status":"live","first_seen_at":"2026-11-06T17:09:39Z","employer_posted_date":"2026-11-06","last_verified_at":"2026-11-09T09:57:43Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"About the Role\nLead the data quality team at an early-stage AI company building infrastructure for reinforcement learning and model evaluation. You will shape how training data is assessed and improved, helping research teams create more reliable and useful AI agents.\nWhat You'll Do\nLead development of systems that evaluate tasks across reinforcement learning environments, synthetic data, benchmarks, and domain-specific workflows.\n\nSet data quality strategy, establish standards, and design experiments to assess agent outputs.\n\nDevelop scalable methods for validating synthetic data, including failure analysis, task mutation checks, and trajectory audits.\n\nWork with research engineers, domain experts, and data vendors to diagnose quality issues and improve data generation.\n\nTurn research insights into production tools, dashboards, validation pipelines, and feedback loops.\n\nMentor research engineers and promote technical rigor, clear communication, and strong execution.\n\nWhat We're Looking For\nAt least 5 years of research or engineering experience focused on AI/ML data evaluation or data quality systems.\n\nExperience leading technical projects or teams through ambiguous problem definition, implementation, and iteration.\n\nAdvanced proficiency in Python, Docker, and Linux.\n\nBackground in AI evaluations, post-training, benchmarks, synthetic data pipelines, or model evaluation infrastructure.\n\nStrong judgment about what makes agent training data realistic, learnable, diverse, reliable, and useful.\n\nExperience designing metrics, experiments, and quality processes, and translating research findings into production systems.\n\nAbility to work with subject-matter experts and convert domain judgment into scalable review or generation workflows.\n\nStrong written communication, attention to detail, and experience in an early-stage startup environment.\n\nCompensation & Benefits\nBase salary: $130,000 to $225,000 annually. Visa sponsorship is available.\nLocation\nOn-site in the United States. 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