{"id":4083717,"url":"https://alion.io/job/clera-research-engineer-synthetic-data-13","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":40,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":6,"computed_at":"2026-11-12T10:38:37Z"}},"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":"2026-11-11T17:15:28Z","employer_posted_date":"2026-11-11","last_verified_at":"2026-11-12T10:49:39Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"About the Role\nJoin a small engineering team building synthetic data systems for AI agents. You will turn domain-specific workflows into scalable training tasks, helping improve how models learn and perform in real-world applications.\nWhat You'll Do\nBuild pipelines that convert professional and technical workflows into realistic, structured training tasks.\n\nCollaborate with subject-matter experts to develop tasks across distinct domains.\n\nDesign methods for generating diverse, realistic, and learnable synthetic tasks.\n\nCreate tools to mutate, validate, and improve generated tasks.\n\nAnalyze agent performance to understand task effectiveness and identify failure modes.\n\nDevelop metrics for 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, building and delivering data pipelines, ML infrastructure, or synthetic data systems.\n\nTechnical education or equivalent technical background, with strong Python skills and experience in Linux environments and Docker.\n\nHands-on experience applying synthetic data methods to build end-to-end generation pipelines for AI or machine learning.\n\nUnderstanding of synthetic data quality, evaluation criteria, and limitations.\n\nExperience building evaluation frameworks, benchmarks, or testing environments for AI agents or large language models.\n\nAbility to independently deliver projects in open-ended settings, with careful attention to data quality and edge cases.\n\nFamiliarity with reinforcement learning, agent workflows, or LLM post-training is valuable.\n\nCompensation & Benefits\nAnnual base salary: $100,000 to $170,000 USD. 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