{"id":1200583,"url":"https://alion.io/job/waabi-senior-staff-ml-ops-engineer","title":"Senior / Staff ML Ops Engineer","company":{"id":4483,"name":"Waabi","domain":"waabi.ai","url":"https://alion.io/company/waabi","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"A","score":92,"open_postings":66,"ghost_share":0,"stale_share":0.106,"repost_share":0,"time_to_fill_p50_days":150,"computed_at":"2026-10-19T05:45:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States","Dallas, United States","Pittsburgh, United States","Phoenix, United States","Toronto, Canada","Canada"],"countries":["US","CA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":249000,"max_usd":509000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":823},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"FSDP","optional":false},{"name":"Helm","optional":false},{"name":"IAM","optional":false},{"name":"Kubernetes","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Pulumi","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Terraform","optional":false},{"name":"Argo Workflows","optional":true},{"name":"Bazel","optional":true},{"name":"Flyte","optional":true},{"name":"Kubeflow","optional":true},{"name":"NCCL","optional":true},{"name":"Ray","optional":true},{"name":"SLURM","optional":true}],"status":"live","first_seen_at":"2026-09-23T23:09:15Z","employer_posted_date":"2026-09-23","last_verified_at":"2026-10-19T16:46:44Z","board_verified":true,"closed_at":null,"days_open":25,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":25},"description":"You will..\nBuild and evolve our training infrastructure on Kubernetes with Infrastructure - GPU scheduling, autoscaling, multi-node distributed jobs, capacity strategy, and the operators and workflow engines that keep long-running training reliable.\nShape the developer-facing surface - CLIs, SDKs, job submission, templates, paved paths - designed with the teams who'll use them. Make the common case one command and keep the uncommon case possible.\nShorten the inner loop. Time to first training run, edit-to-signal latency, local iteration before a job hits the cluster, fast failure over slow mystery. Measure it, publish it, drive it down.\nEvangelize best-in-class tooling and frameworks. Track what the ecosystem is shipping, evaluate honestly, and make the case with working prototypes and migration paths - or say plainly when a shiny thing isn't worth the switching cost.\nStrengthen the data and artifact layer. Dataset versioning, sharding, and high-throughput loading of large multimodal sensor data, so jobs saturate GPUs instead of waiting on I/O.\nTurn one-off Python into durable tooling - tested, documented, observable libraries, CLIs, and services with sane defaults, and deletions where they're overdue.\nMake experiments legible, with the teams who live in them: experiment hygiene, dashboards researchers trust, a real model registry, and lineage from dataset to checkpoint to simulation result.\nShip CI/CD for models alongside autonomy and simulation, so a model change is validated the same way a code change is.\nBuild observability across the ML stack - utilization, throughput, failure modes, queue times, cost per experiment. When a job fails at 3am on node 47, the researcher should find out why without you.\nTreat docs, onboarding, and support as product surface - golden-path guides, a new researcher productive on day two, office hours that turn repeat questions into shipped fixes.\nDrive adoption, not just availability. Prototype with real users, watch them work, iterate. A tool nobody adopts didn't ship.\nMake the platform boringly reliable - fewer failures, faster recovery, and none of the manual steps that quietly cost a team days.\nBuild guardrails that don't feel like walls, with Security, IT, and Infrastructure: access controls, data handling, and cost governance that hold up in an IP-sensitive environment while staying self-serve.\nQualifications:\n5+ years of software or infrastructure engineering, including tools or platforms used by other engineers and operating ML or data-intensive production systems.\nHands-on Kubernetes expertise - GPU scheduling, autoscaling, Helm or equivalent, networking fundamentals, and the ability to debug a cluster under load rather than restart it.\nExcellent Python, and a track record of designing APIs and CLIs other people enjoy using.Practical AWS depth: object storage at scale, IAM, GPU compute, networking, cost management, and infrastructure as code (Terraform, Pulumi, or similar).\n\nDistributed training in PyTorch (DDP, FSDP, or similar), plus experiment tracking and model registry tooling - from the perspective of someone who made them pleasant for others to use.\nFluency with containers, CI/CD, and modern build systems, including large monorepos.\nThe ability to influence without authority: evaluate a framework on its merits, pilot it credibly, and persuade skeptical senior engineers to change how they work.\nA collaborative default - you'd rather co-own a system than draw a boundary around your part of it.\nUser empathy: you'd rather fix the third-most-interesting problem blocking ten people than the most interesting one blocking nobody.\nStrong product instincts, strong writing, and comfort operating autonomously in ambiguous territory.\nPassionate about self-driving technologies and frontier AI, and about what a small, world-class team can do with the right infrastructure.\nBonus/nice to have:\nInternal developer platform, research platform, or DevEx work - with a story about a tool whose adoption you grew from zero.\nLarge-scale distributed GPU training: hundreds to thousands of accelerators, NCCL, high-performance cluster networking, collective communication tuning.\nHigh-throughput loading of LiDAR or camera data, and formats such as Parquet or WebDataset.\nWorkflow and scheduling systems - Argo Workflows, Ray, Flyte, Kubeflow, or Slurm.\nBuild-system depth (Bazel or similar), including remote caching in a monorepo.\nSimulation infrastructure or large-scale batch evaluation pipelines.\nBackground in ML, robotics, or autonomous systems infrastructure.\nSecurity- and IP-sensitive production environments.\nOpen-source contributions to ML infrastructure or developer tools.","description_format":"text","description_chars":4680,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"},{"name":"Canada","iso":"CA","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Autonomous Driving"],"lifecycle":[{"event":"open","at":"2026-09-24T20:41:00Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 9","filings_12m":9,"filings_prev_12m":0,"green_card_filings_12m":0,"median_offered_wage_usd":172500,"route":null,"cap_exempt":false,"checked_at":"2026-10-15T11:24:14+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)"],"filings_for_role_12m":6}],"liveness":{"score":89,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.888,"p_room":1,"age_days":25,"expected_fill_days":150,"reasons":["conf:0","velocity","win:early"],"computed_at":"2026-10-19T05:45:15Z"},"pay":null,"html_url":"https://alion.io/job/waabi-senior-staff-ml-ops-engineer","json_url":"https://alion.io/job/waabi-senior-staff-ml-ops-engineer.json","meta":{"generated_at":"2026-10-19T19:14:01Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":15252,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-20T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":4483},"rest":"https://alion.io/mcp/rest/get_company?id=4483"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fwaabi-senior-staff-ml-ops-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fwaabi-senior-staff-ml-ops-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fwaabi-senior-staff-ml-ops-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/waabi-senior-staff-ml-ops-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fwaabi-senior-staff-ml-ops-engineer"}]}