{"id":1200513,"url":"https://alion.io/job/universalagi-ml-platform-engineer","title":"ML Platform Engineer","company":{"id":8996,"name":"Universalagi","domain":"universalagi.com","url":"https://alion.io/company/universalagi","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"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"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":164000,"max_usd":357000,"period":"year","method":"role_country_seniority_unknown","sample_n":2986},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Fine-tuning","optional":false},{"name":"HPC","optional":false},{"name":"Kubernetes","optional":false},{"name":"Ray","optional":false},{"name":"SLURM","optional":false},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"GCP","optional":true}],"status":"live","first_seen_at":"2026-09-24T19:24:09Z","employer_posted_date":"2026-09-24","last_verified_at":"2026-09-30T00:47:34Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"description":"San Francisco | 5 Days Onsite\nLocation: Onsite in San Francisco\nCompensation: Competitive Salary + Equity\nWho We Are\nEngineering simulation is one of the last major categories of software that AI hasn't rebuilt. The tools used to design aircraft, ships, reservoirs, and medical devices still run on numerical methods that are decades old, and an engineer can wait a full day for a single answer. UniversalAGI is building foundation models that learn physics directly from data, and they are already running in early deployments on real computational fluid dynamics and reservoir engineering problems for some of the largest industrial and defense organizations in the world.\nWe are a team of 25 researchers and engineers in San Francisco backed by Elad Gil (#1 Solo VC), Eric Schmidt (former Google CEO), Prith Banerjee (ANSYS CTO), Ion Stoica (Databricks Founder), Jared Kushner (former Senior Advisor to the President), David Patterson (Turing Award Winner), and Luis Videgaray (former Foreign and Finance Minister of Mexico).\nAbout the Role\nUniversalAGI is hiring a ML Platform Engineer to build and own the execution platform powering our research and customer deployments: data generation + simulation orchestration + training/fine-tuning infrastructure + benchmarking pipelines + production deployments in customer environments.\nYou’ll work closely with the CEO and founding team to turn research into repeatable, scalable, reliable systems - internally and in customer infrastructure. This is a “ship outcomes” role: your work directly determines how fast we can iterate, how reproducible our results are, and how reliably we deliver in production.\nWhat You’ll Do\nBuild the foundation platform (internal):\nBuild and operate scalable infrastructure for data generation and simulation workflows (job orchestration, scheduling, queues, retries, observability).\n\nBuild reproducible pipelines for training/fine-tuning and benchmarking (artifact/version management, experiment tracking, dataset lineage).\n\nOwn cost/performance tradeoffs across compute, storage, networking, and runtime efficiency.\n\nDeploy to customers (external):\nLead deployments of our stack into customer cloud/on-prem environments (AWS/GCP/Azure + hybrid), including secure networking, permissions, and data movement.\n\nBuild robust deployment patterns: environment provisioning, CI/CD, rollbacks, monitoring, and incident response.\n\nPartner with customers to ensure reliability and repeatability under real-world constraints (security, compliance, infra limits, data governance).\n\nQualifications\nStrong software engineering skills (clean code, debugging, reliability, reproducibility).\n\nHands-on experience building/operating infrastructure for ML/compute-heavy workflows: pipelines, job orchestration, GPU compute, storage, CI/CD, monitoring.\n\nOlympic athlete mindset: You have high standards for yourself and are obsessed with measurable improvement on the metrics you are delivering to customers.\n\nResourcefulness: you know when to do the “quick & correct” fix vs. when to invest in a robust solution, and you can justify the tradeoff with impact/\n\nOwnership: Comfortable owning work end-to-end and being accountable for measurable outcomes.\n\nBonus Qualifications\nExperience with workflow orchestration (e.g., Ray, Kubernetes, Slurm).\n\nExperience with GPU infrastructure and distributed training systems.\n\nExperience building evaluation/benchmarking frameworks with strong reproducibility guarantees.\n\nExperience deploying into regulated / security-sensitive environments (gov/defense/enterprise).\n\nExperience with simulation/HPC pipelines (CFD, meshing, batch workloads) is a plus but not required.\n\nExperience in an FDE-style / delivery execution role (or similar “ship results fast” environments).\n\nCultural Fit\nTechnical Respect: Ability to earn respect through hands-on technical contribution\n\nIntensity: Thrives in our unusually intense culture - willing to grind when needed\n\nCustomer Obsession: Passionate about solving real customer problems, not just cool tech\n\nDeep Work: Values long, uninterrupted periods of focused work over meetings\n\nHigh Availability: Ready to be deeply involved whenever critical issues arise\n\nCommunication: Can translate complex technical concepts to customers and team\n\nGrowth Mindset: Embraces the compounding returns of intelligence and continuous learning\n\nStartup Mindset: Comfortable with ambiguity, rapid change, and wearing multiple hats\n\nWork Ethic: Willing to put in the extra hours when needed to hit critical milestones\n\nTeam Player: Collaborative approach with low ego and high accountability\n\nWhat We Offer\nOpportunity to shape the technical foundation of a rapidly growing foundational AI company.\n\nWork on cutting-edge industrial AI problems with immediate real-world impact.\n\nDirect collaboration with the founder & CEO and ability to influence company strategy\n\nCompetitive compensation with significant equity upside.\n\nIn-person first culture - 5 days a week in office with a team that values face-to-face collaboration.\n\nAccess to world-class investors and advisors in the AI space.\n\nBenefits\nWe provide great benefits, including:\nCompetitive compensation and equity.\n\nCompetitive health, dental, vision benefits paid by the company.\n\n401(k) plan offering.\n\nFlexible vacation.\n\nTeam Building & Fun Activities.\n\nGreat scope, ownership and impact.\n\nAI tools stipend.\n\nMonthly commute stipend.\n\nMonthly wellness / fitness stipend.\n\nDaily office lunch & dinner covered by the company.\n\nImmigration support.\n\nHow We’re Different\n“The credit belongs to the man who is actually in the arena, whose face is marred by dust and sweat and blood; who strives valiantly; who errs, who comes short again and again... who at the best knows in the end the triumph of high achievement, and who at the worst, if he fails, at least fails while daring greatly.\" - Teddy Roosevelt\nAt our core, we believe in being “in the arena.” We are builders, problem solvers, and risk-takers who show up every day ready to put in the work: to sweat, to struggle, and to push past our limits. We know that real progress comes with missteps, iteration, and resilience. We embrace that journey fully knowing that daring greatly is the only way to create something truly meaningful.\nIf you're ready to join the future of physics simulation, push creative boundaries, and deliver impact, UniversalAGI is the place for you.","description_format":"text","description_chars":6415,"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":["Continuous learning","Equity"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Machine Learning","Simulation & Digital Twin Software","AI for Science"],"lifecycle":[{"event":"open","at":"2026-09-24T20:37:54Z"}],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":5,"expected_fill_days":30,"reasons":["conf:4","velocity","win:early"],"computed_at":"2026-09-30T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/universalagi-ml-platform-engineer","json_url":"https://alion.io/job/universalagi-ml-platform-engineer.json","meta":{"generated_at":"2026-09-30T06:10:30Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":4228,"day_limit":5000,"remaining_today":772,"minute_limit":60,"resets_at":"2026-10-01T00:00:00Z"}}}