{"id":883379,"url":"https://alion.io/job/fieldai-senior-data-platform-engineer","title":"Senior Data Platform Engineer","company":{"id":672883,"name":"FieldAI","domain":"fieldai.com","url":"https://alion.io/company/fieldai","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":82,"open_postings":103,"ghost_share":0.223,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":62,"computed_at":"2026-10-31T05:58:55Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Irvine, United States","Seattle, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":138000,"max_usd":261000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1343},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon S3","optional":false},{"name":"C++","optional":false},{"name":"Dagster","optional":false},{"name":"Go","optional":false},{"name":"Java","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Ray","optional":false},{"name":"Spark","optional":false}],"status":"live","first_seen_at":"2026-09-13T22:58:29Z","employer_posted_date":"2026-09-13","last_verified_at":"2026-11-01T03:14:05Z","board_verified":true,"closed_at":null,"days_open":48,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":47},"description":"About the Role\nWe are building the data foundation that powers the full machine learning lifecycle for autonomous robotics. Our robots generate large-scale, multimodal datasets across real-world deployments, and turning that raw experience into reliable, discoverable, high-quality ML data is a core part of improving our autonomy systems.\nAs a Data Platform Engineer, you will help design and build the platform that manages data from ingestion through processing, validation, labeling, dataset generation, training, and evaluation.\nThis is not a traditional analytics data engineering role. You will work closely with ML engineers, researchers, labeling teams, robotics engineers, and infrastructure engineers to build scalable systems for robotics and ML data.\nWhat You'll Do\nDesign and build scalable data architecture for large-scale multimodal robotics and ML datasets.\nBuild abstractions and services for ingestion, processing, datasets, metadata, lineage, and data quality.\nDevelop reliable pipelines for transforming raw robot data into versioned, ML-ready data products.\nDefine data models, schemas, contracts, and lifecycle states across data-processing workflows.\nBuild systems for tracking provenance and lineage across raw data, derived artifacts, labels, datasets, and downstream ML workloads.\nDevelop automated validation and data-quality frameworks that detect incomplete, corrupted, or unusable data early.\nDesign for incremental processing, reprocessing, backfills, and versioned transformations.\nImprove observability and failure diagnosis across complex data workflows.\nPartner with ML and robotics teams to understand domain-specific data requirements and turn recurring patterns into reusable platform capabilities.\nWork closely with infrastructure/platform teams on storage, compute, orchestration, reliability, and scalability.\nWhat We're Looking For\nStrong experience building production data platforms or large-scale data-processing systems.\nStrong software engineering skills, preferably Python and/or C++/Java/Go.\nExperience with distributed data processing and workflow orchestration.\nExperience with data lakes/lakehouses, object storage, metadata systems, schemas, and data versioning.\nStrong understanding of data quality, lineage, reproducibility, and reliable pipeline design.\nExperience with technologies such as S3, Airflow/Dagster, Spark/Ray, Kubernetes, Parquet, or similar systems.\nAbility to work across ambiguous organizational and technical boundaries.\nStrong systems-design and engineering judgment.\nNice to Have\nExperience 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