{"id":2290216,"url":"https://alion.io/job/luvu-inc-founding-engineer-physical-ai-infrastructure","title":"Founding Engineer (Physical AI Infrastructure)","company":{"id":4398972,"name":"Basis","domain":"withbasis.co","url":"https://alion.io/company/withbasis-2","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":95,"open_postings":28,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":73,"computed_at":"2026-10-24T06:02:35Z"}},"role":"Backend","role_family":"Backend","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":156000,"max_usd":325000,"period":"year","method":"role_country_seniority_unknown","sample_n":7766},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Apache Kafka","optional":false},{"name":"ClickHouse","optional":false},{"name":"Go","optional":false},{"name":"Kubernetes","optional":false},{"name":"Multimodal AI","optional":false},{"name":"NATS","optional":false},{"name":"Physical AI","optional":false},{"name":"Python","optional":false},{"name":"Ray","optional":false},{"name":"Rust","optional":false},{"name":"SLURM","optional":false},{"name":"Time Series","optional":false}],"status":"live","first_seen_at":"2026-08-07T20:51:36Z","employer_posted_date":"2026-08-07","last_verified_at":"2026-10-24T07:45:09Z","board_verified":true,"closed_at":null,"days_open":77,"trust":{"level":"ok","repost_count":1,"flags":[],"days_open":77},"description":"The role:\nBuild the control plane and data plane for physical AI.\nMunari must run training, simulation, evaluation, data processing, and inference workloads across public cloud, customer-owned infrastructure, robotics labs, and fleets of edge devices. It must handle GPUs, enormous multimodal datasets, unreliable connectivity, long-running workloads, and machines that cannot simply be restarted whenever something goes wrong.\nThis is not a conventional DevOps role and it is not a YAML-only role. You will write the systems software that makes physical AI infrastructure feel closer to a programmable platform than a pile of bespoke operations.\nWhat you will work on:\nBuild a unified execution platform for simulation, policy evaluation, model training, data processing, and deployment.\n\nDesign workload scheduling and orchestration across CPUs, GPUs, Kubernetes clusters, bare metal, on-prem environments, and edge devices.\n\nBuild reliable systems for checkpointing, retrying, resuming, scaling, and observing long-running robotics workloads.\n\nCreate the data infrastructure for video, audio, sensor streams, telemetry, trajectories, policy traces, incidents, and human interventions.\n\nDesign storage, indexing, lineage, replay, retention, and movement of large multimodal datasets.\n\nBuild the APIs, SDKs, CLIs, and deployment workflows that make the underlying infrastructure simple for robotics engineers and researchers.\n\nOwn platform reliability, observability, capacity management, incident response, security, tenancy, and cost efficiency.\n\nSupport disconnected, bandwidth-constrained, private, and potentially air-gapped customer environments.\n\nConnect cloud-side infrastructure to the Munari edge runtime and make the entire system operable as one platform.\n\nYou may be a strong fit if:\nYou have built or operated large-scale distributed systems where reliability genuinely mattered.\n\nYou have strong experience with Kubernetes, infrastructure as code, networking, storage, security, and cloud architecture.\n\nYou understand GPU infrastructure, distributed training, batch systems, MLOps, or high-performance computing.\n\nYou have worked with high-volume streaming, time-series, image, video, or scientific data.\n\nYou write production software in Rust, Go, Python, or a similar systems language rather than treating infrastructure as configuration alone.\n\nYou are comfortable debugging across an application, container, scheduler, network, host, GPU, and storage system.\n\nYou care equally about the internal architecture and the developer experience exposed to the customer.\n\nExperience with NVIDIA infrastructure, Slurm, Ray, Kueue, Temporal, Kafka, NATS, ClickHouse, Parquet, object storage, multi-cluster Kubernetes, or edge fleet management is useful but not mandatory.","description_format":"text","description_chars":2782,"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":["Artificial Intelligence","AI Training Data & 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