{"id":2473176,"url":"https://alion.io/job/clera-research-engineer-qc-automation-8","title":"Research Engineer, QC Automation","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":30,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":6,"computed_at":"2026-10-19T05:45:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":100000,"max":170000,"currency":"USD","period":"year","gross":null,"usd_annual":170000},"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Docker","optional":false},{"name":"Linux","optional":false},{"name":"Python","optional":false},{"name":"Reinforcement Learning","optional":false}],"status":"live","first_seen_at":"2026-10-13T17:04:51Z","employer_posted_date":"2026-10-13","last_verified_at":"2026-10-19T08:39:27Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"description":"About the Role\nJoin an early-stage AI evaluation infrastructure startup as a Research Engineer focused on quality control automation. You will help build systems that make high-quality training data more scalable, combining careful human judgment with reliable measurement and automation.\nWhat You'll Do\nBuild and improve quality control systems for training data created within reinforcement learning environments.\n\nDefine quality standards and design experiments and metrics to assess agent outputs.\n\nDevelop auditing approaches, including sampling strategies and rule-based or model-assisted validation pipelines.\n\nWork with data vendors to diagnose quality issues, understand agent failure modes, and improve data generation processes.\n\nTurn quality control findings into improvements to internal infrastructure and vendor-facing tools.\n\nWhat We're Looking For\nTwo to four years of experience in a research engineering or similar role, including hands-on work in quality control automation.\n\nProficiency with Python, Docker, and Linux, plus experience building scalable data validation and automated QA/QC systems end to end.\n\nExperience with benchmarks and evaluations for reinforcement learning training data, including realistic tasks, reliable rubrics, and useful training trajectories.\n\nAbility to define and measure training data quality, and to build quality control systems grounded in human judgment rather than heavy reliance on large language models.\n\nKnowledge of statistics and experience designing metrics, experiments, and quality assurance processes.\n\nStrong communication skills, curiosity, and the ability to make progress independently in ambiguous, fast-moving environments.\n\nCompensation & Benefits\nAnnual salary range: $100,000 to $170,000 USD. Visa sponsorship is available.\nLocation\nOn-site in Singapore. 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