{"id":2948523,"url":"https://alion.io/job/clera-research-engineer-synthetic-data-10","title":"Research Engineer, Synthetic Data","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":"2026-10-26T05:57:25Z"}},"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":"LLM","optional":false},{"name":"Machine Learning","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":"2026-10-20T17:09:51Z","employer_posted_date":"2026-10-20","last_verified_at":"2026-10-26T23:14:52Z","board_verified":true,"closed_at":null,"days_open":6,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":6},"description":"About the Role\nJoin a research and engineering team building synthetic data systems for AI agents. You will develop methods and infrastructure that turn real-world workflows into high-quality training tasks, helping expand and evaluate model capabilities.\nWhat You'll Do\nBuild pipelines that transform domain-specific workflows into realistic, structured, and challenging synthetic training tasks.\n\nCollaborate with subject-matter experts to create tasks across professional and technical domains.\n\nDesign methods for generating diverse, realistic, and learnable tasks.\n\nDevelop tools to mutate, validate, and improve synthetic tasks.\n\nAnalyze model and agent performance to understand task effectiveness and identify failure modes.\n\nCreate metrics to assess task diversity, realism, learnability, and overall quality.\n\nWhat We're Looking For\nTwo or more years of experience in software engineering, machine learning engineering, or AI research, with a technical background.\n\nHands-on experience applying synthetic data methods to build end-to-end data generation pipelines for AI or machine learning.\n\nProficiency in Python, Linux, and containerization tools such as Docker.\n\nExperience with evaluation frameworks, benchmarks, or testing environments for AI agents or large language models.\n\nUnderstanding of synthetic data quality, evaluation metrics, and limitations, backed by research or production work.\n\nExperience building automated systems to generate, validate, mutate, or process structured data at scale.\n\nAbility to own technical projects from conception through delivery in loosely defined problem spaces.\n\nStrong attention to detail, communication skills, and ability to reason about task design, scoring, and edge cases.\n\nFamiliarity with reinforcement learning, agent workflows, or LLM post-training is helpful.\n\nCompensation & Benefits\nAnnual salary range: $100,000 to $170,000 USD. 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