{"id":3853728,"url":"https://alion.io/job/planet-pharma-aiml-scientist-agent-platform-p4021","title":"AI/ML Scientist, Agent Platform (P4021)","company":{"id":4467183,"name":"Planet Pharma","domain":"planet-pharma.com","url":"https://alion.io/company/planet-pharma","size_band":null,"is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Schema","truth_index":{"grade":"B","score":80,"open_postings":12,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":5,"computed_at":"2026-11-11T05:49:22Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"contractor","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Redwood City, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Databricks","optional":false},{"name":"Git","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Structured Outputs","optional":false},{"name":"LLM","optional":true},{"name":"OpenAI Agents SDK","optional":true}],"status":"live","first_seen_at":"2026-11-04T00:00:00Z","employer_posted_date":"2026-11-05","last_verified_at":"2026-11-11T19:01:26Z","board_verified":true,"closed_at":null,"days_open":7,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":7},"description":"AI/ML Scientist, Agent Platform\nThe Opportunity:\nSeeking a hands-on Contract AI/ML Scientist to build reliable AI agent workflows for scientific data extraction and analysis. The primary focus is agent platform and harness engineering, initially supporting agentic structuring of data from CRO reports focused on TCI synthetic chemistry, with flexibility to support additional agent development projects. Working with team scientists and data engineers, this individual contributor will implement defined requirements, build reusable tools, and prepare prototypes for deployment.\nResponsibilities:\nIntegrate model APIs, tools, and agent orchestration into maintainable workflows for tasks defined by team scientists.\nConnect data ingestion, agent processing, and structured outputs through reliable pipelines, including delivery to Databricks.\nImplement typed output schemas and deterministic validation checks to detect invalid outputs and support scientific review.\nBuild evaluation infrastructure for benchmark runs, result comparisons, and performance assessments led by scientists and collaborators.\nManage storage, compute, tracing, failure recovery, and reproducibility; monitor and improve workflow cost and latency.\nMaintain shared repositories, tests, packaging, technical documentation, and reproducible development environments.\nPartner with data engineers and RevCore on deployment preparation, secure handling of internal data, and operational handoff.\nCommunicate progress, limitations, and technical tradeoffs; incorporate feedback and adapt to priorities across scientific projects.\nRequired Skills, Experience and Education:\nDegree in computer science, software engineering, data science, or a related field, or equivalent practical experience.\nStrong Python programming and software design skills, including with agentic coding tools, with experience delivering maintainable applications or computational workflows.\nExperience integrating APIs and implementing typed schemas, automated tests, debugging, and dependency management.\nExperience with Git-based development, code review, packaging, and clear technical documentation.\nExperience building data pipelines and reliable workflows, including logging, failure recovery, and reproducibility.\nBasic fluency in machine learning and data quality, and sufficient chemistry and biology understanding to follow scientific requirements.\nAbility to communicate uncertainty and technical concepts clearly to colleagues with different scientific and software backgrounds.\nAbility to independently execute defined work, meet agreed milestones, incorporate feedback, and handle internal data securely.\nPreferred Skills:\nExperience developing LLM applications or agent workflows using the Agents SDK or comparable orchestration frameworks.\nExperience with Databricks, cloud platforms, or high-performance computing environments.\nExperience with tracing, observability, rate-limit handling, and cost or latency optimization for model APIs.\nExperience building evaluation tooling, structured-output validation, or scientific-data applications.\nExperience using AI-assisted coding tools with code review and testing or delivering projects in biotechnology or consulting.\nExperience with synthetic and/or medicinal chemistry a 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