{"id":3419420,"url":"https://alion.io/job/clera-research-engineer-synthetic-data-11","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":33,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":6,"computed_at":"2026-11-01T06:10:25Z"}},"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":"2026-10-28T17:07:57Z","employer_posted_date":"2026-10-28","last_verified_at":"2026-11-01T14:55:56Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"About the Role\nBuild synthetic data systems that turn specialized workflows into realistic, scalable training tasks for AI agents. You will work with an engineering team to develop task generation, validation, and evaluation methods that help improve model capabilities.\nWhat You'll Do\nBuild pipelines that transform domain-specific workflows into structured and challenging synthetic training tasks.\n\nCollaborate with subject-matter experts to create tasks across professional and technical domains.\n\nDevelop methods for generating diverse, realistic, and learnable tasks.\n\nCreate tooling to mutate, validate, and improve synthetic tasks.\n\nAnalyze model and agent performance to identify what tasks teach and where they fail.\n\nDevelop metrics for task diversity, realism, learnability, and quality.\n\nWhat We're Looking For\nAt least 2 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 background, with hands-on experience developing end-to-end synthetic data pipelines for AI or machine learning applications.\n\nProficiency in Python, Linux development, and containerization tools such as Docker.\n\nExperience evaluating synthetic data quality, including diversity, realism, learnability, and limitations.\n\nExperience building evaluation frameworks, benchmarks, or testing environments for AI agents or large language models.\n\nAbility to independently deliver technical projects with minimal predefined requirements, and to identify subtle data inconsistencies and edge cases.\n\nStrong reasoning about task design, scoring, and failure modes; familiarity with reinforcement learning, agent workflows, or LLM post-training is useful.\n\nClear communication skills for collaboration across time zones.\n\nCompensation & Benefits\nBase salary: $100,000 to $170,000 USD annually. 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