{"id":1647992,"url":"https://alion.io/job/institute-of-foundation-models-machine-learning-engineer-world-model","title":"Machine Learning Engineer – World Model","company":{"id":99421,"name":"Institute of Foundation Models","domain":"ifm.ai","url":"https://alion.io/company/institute-of-foundation-models","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"A","score":95,"open_postings":44,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":483,"computed_at":"2026-10-19T05:45:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Sunnyvale, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":150000,"max":450000,"currency":"USD","period":"year","gross":null,"usd_annual":450000},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Docker","optional":false},{"name":"Git","optional":false},{"name":"Gradio","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"Open WebUI","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"Ray","optional":false},{"name":"Spark","optional":false},{"name":"World Models","optional":false},{"name":"Claude Code","optional":true},{"name":"Cursor","optional":true},{"name":"Multimodal AI","optional":true},{"name":"OpenAI Codex","optional":true}],"status":"live","first_seen_at":"2026-05-05T22:13:30Z","employer_posted_date":"2026-05-05","last_verified_at":"2026-10-20T03:57:31Z","board_verified":true,"closed_at":null,"days_open":167,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":167},"description":"About the Institute of Foundation Models\nWe are a dedicated research lab for building, understanding, using, and risk-managing foundation models. Our mandate is to advance research, nurture the next generation of AI builders, and drive transformative contributions to a knowledge-driven economy.\nAs part of our team, you’ll have the opportunity to work on the core of cutting-edge foundation model training, alongside world-class researchers, data scientists, and engineers, tackling the most fundamental and impactful challenges in AI development. You will participate in the development of groundbreaking AI solutions that have the potential to reshape entire industries. Strategic and innovative problem-solving skills will be instrumental in establishing MBZUAI as a global hub for high-performance computing in deep learning, driving impactful discoveries that inspire the next generation of AI pioneers.\nThe Team\nWe are the AllWorld Team under the Institute of Foundation Model (IFM) at MBZUAI. At AllWorld, we are pioneering the development of the PAN (Physical, Agentic, and Networked) world models-the next-generation foundation models to unlock machine intelligence beyond lingual.\nOur mission is to tackle the fundamental challenges of world modeling and establish a new paradigm for next-generation machine reasoning. We are looking for passionate individuals who share our vision and are eager to push the boundaries of AI together.\nRole Overview\nWe’re looking for a Machine Learning Engineer focused on ML infrastructure and MLOpsto design and operate the systems that power our research environment. You’ll build scalable, reliable, and observable cloud infrastructure, working closely with researchers to support data pipelines, experimentation, and evaluation workflows.\n\nThis role balances fast-moving research needs with production-grade systems, ensuring that experimental work can scale reliably when needed.\n\nKey Responsibilities\nDesign, build, and operate scalable ML infrastructure on AWS (e.g., compute, storage, networking, access control).\n\nDevelop and maintain MLOps workflows for data versioning\n\nBuild and manage distributed systems for large-scale data processing (filtering, captioning, etc.) and model evaluation.\n\nOwn architecture decisions for ML infrastructure and drive best practices in reliability, scalability, and cost efficiency.\n\nImplement observability across systems, including monitoring, logging, and alerting.\n\nIntegrate OpenWebUI, Gradio, or similar UIs for data quality assurance\n\nBuild and maintain dashboards for experiment tracking and system 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