{"id":127974,"url":"https://alion.io/job/physical-intelligence-ml-infra-engineer-data-systems","title":"ML Infra Engineer, Data Systems","company":{"id":48436,"name":"Physical Intelligence","domain":"pi.website","url":"https://alion.io/company/physicalintelligence","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":80,"open_postings":41,"ghost_share":0.341,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":55,"computed_at":"2026-11-09T06:03:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":164000,"max_usd":364000,"period":"year","method":"role_country_seniority_unknown","sample_n":4425},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"ClickHouse","optional":true},{"name":"Flink","optional":true},{"name":"Ray","optional":true}],"status":"live","first_seen_at":"2026-07-24T22:44:51Z","employer_posted_date":"2026-09-01","last_verified_at":"2026-11-09T13:08:09Z","board_verified":true,"closed_at":null,"days_open":107,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":107},"description":"Physical Intelligence is bringing general-purpose AI into the physical world. We are a group of engineers, scientists, roboticists, and company builders developing foundation models and learning algorithms to power the robots of today and the physically-actuated devices of the future.\nAs an ML Infra Engineer (Data Systems), you’ll build and operate the data infrastructure that powers large-scale robot learning. Your systems will sit directly between raw data sources and training/evaluation, enabling us to move faster while maintaining performance, correctness, and reliability at scale.\nThis is a systems role at the intersection of distributed systems, storage, and machine learning infrastructure.\nThe Team\nThe Infrastructure organization builds the foundations that make large-scale learning possible at PI. This includes training systems, data platforms, evaluation pipelines, and the tooling that allows researchers and roboticists to work with massive datasets safely and efficiently.\nIn This Role You Will\nData Ingestion & Processing: Design and build high-throughput pipelines that validate, transform, and featurize raw multimodal data.\n\nBatch & Streaming Systems: Operate large-scale batch and streaming workflows over massive datasets.\n\nStorage Systems: Design object storage layouts, metadata systems, and efficient access patterns; choose file formats with performance and scalability in mind.\n\nData Lifecycle Management: Build systems for backfills, dataset rebuilds, garbage collection, and large-scale transformations.\n\nTraining-Time Performance: Optimize dataloaders, sharding, prefetching, caching, and throughput to reduce time from data arrival → model training.\n\nMetadata & Indexing: Build scalable metadata stores for datasets, annotations, and training artifacts.\n\nData Movement: Move petabytes efficiently across clusters and environments.\n\nOperational Correctness: Implement observability, validation, and guardrails to prevent silent data regressions.\n\nCross-Functional Collaboration: Work closely with cross-functional teams of researchers, engineers and roboticists to translate evolving data needs into robust systems.\n\nWhat We Hope You’ll Bring\nStrong software engineering fundamentals.\n\nExperience building distributed systems or large-scale data pipelines.\n\nComfort reasoning about performance, memory, I/O, and storage efficiency.\n\nFamiliarity with batch and/or streaming processing systems.\n\nExperience with object storage systems and data format tradeoffs.\n\nOwnership mindset: design, build, operate, and iterate on systems end-to-end.\n\nEnjoy working closely with researchers and unblocking fast-moving projects.\n\nBonus Points If You Have\nExperience with large ML training pipelines or dataloading systems.\n\nKnowledge of columnar or custom data formats.\n\nExperience with systems like ClickHouse, Ray, Flink, Spark, or similar.\n\nHands-on experience operating petabyte-scale datasets.\n\nDebugging and fixing performance bottlenecks in data-heavy systems.\n\nPursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.","description_format":"text","description_chars":3135,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Robotics AI","Foundation Models","AI Research Labs"],"lifecycle":[{"event":"open","at":"2026-07-24T22:44:51Z"}],"visa":[],"liveness":{"score":18,"band":"cold","label":"Long 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