{"id":3834377,"url":"https://alion.io/job/clera-research-engineer-benchmarks-11","title":"Research Engineer, Benchmarks","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":"2026-11-08T05:47:12Z"}},"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":"AI Agents","optional":false},{"name":"Docker","optional":false},{"name":"Linux","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Reinforcement Learning","optional":false}],"status":"live","first_seen_at":"2026-11-05T17:04:22Z","employer_posted_date":"2026-11-05","last_verified_at":"2026-11-08T08:26:38Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"About the Role\nJoin a small, technical team at an early-stage AI evaluation company building benchmarks for frontier AI agents. You will own the design and implementation of rigorous, domain-specific evaluations that help research teams and customers understand real-world agent performance.\nWhat You'll Do\nDesign, implement, and maintain benchmarks for evaluating AI agents on domain-specific tasks.\n\nWork with subject-matter experts to translate real workflows into realistic tasks and evaluation criteria.\n\nBuild reliable infrastructure for running models and agents against benchmark tasks at scale.\n\nDevelop metrics and analyses to assess benchmark difficulty, reliability, and failure modes.\n\nValidate how benchmark results relate to real-world evaluations and user needs.\n\nWrite clear technical documentation and reports communicating benchmark methods and results.\n\nWhat We're Looking For\nTwo to four years of experience in research engineering, machine learning engineering, or a related technical role, including at least two years building or delivering AI benchmarks, evaluation infrastructure, or agent environments.\n\nStrong proficiency in Python, Docker, and Linux for research or production infrastructure.\n\nHands-on experience designing, implementing, and operating evaluations for AI agents or large language models.\n\nExperience building infrastructure to run models or agents reliably, and developing metrics, statistical analyses, or validation studies.\n\nAbility to collaborate with subject-matter experts and analyze workflows across different domains to create useful evaluation tasks.\n\nStrong technical writing, attention to detail, first-principles reasoning, and comfort working independently in a fast-paced startup.\n\nPublished research or technical writing on benchmarking, model evaluation, or failure modes is valued. 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