{"id":1112,"url":"https://alion.io/job/synthesia-principal-ml-platform-engineer","title":"Principal ML Platform Engineer","company":{"id":147,"name":"Synthesia","domain":"synthesia.io","url":"https://alion.io/company/synthesia","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":93,"open_postings":31,"ghost_share":0,"stale_share":0.097,"repost_share":0,"time_to_fill_p50_days":97,"computed_at":"2026-10-21T05:46:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_regions","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["DE","FR","GB","AT","BE","BG","HR","CZ","DK","FI","HU","IE","IT","NL","NO","PL","RO","ES","SE","CH","EE","GR","IS","LV","LI","LT","LU","MT","PT","SK","SI"],"hiring_countries_total":31,"salary":null,"salary_estimate":{"min_usd":135000,"max_usd":331000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1199},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Datadog","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Kubernetes","optional":false},{"name":"Linux","optional":false},{"name":"Miro","optional":false},{"name":"Python","optional":false},{"name":"Terraform","optional":false},{"name":"Webflow","optional":false},{"name":"LLM","optional":true}],"status":"live","first_seen_at":"2026-04-08T09:40:09Z","employer_posted_date":"2026-04-08","last_verified_at":"2026-10-21T14:13:55Z","board_verified":true,"closed_at":null,"days_open":196,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":195},"description":"Synthesia is the world’s leading AI video platform for business, used by over 90% of the Fortune 100. Founded in 2017, the company is headquartered in London, with offices and teams across Europe and the US.\nAs AI continues to shape the way we live and work, Synthesia develops products to enhance visual communication and enterprise skill development, helping people work better and stay at the center of successful organizations.\nFollowing our recent Series E funding round, where we raised $200 million, our valuation stands at $4 billion. Our total funding exceeds $530 million from premier investors including Accel, NVentures (Nvidia's VC arm), Kleiner Perkins, GV, and Evantic Capital, alongside the founders and operators of Stripe, Datadog, Miro, and Webflow.\nWe’re looking for a Principal Engineer to join the ML Platform team at Synthesia.\nOur team builds and operates the systems that allow researchers and product teams to train, serve, and deploy generative models reliably and efficiently. This includes research infrastructure, production serving systems, internal tooling, and the platform interfaces that connect them. A growing part of our mission is making these systems more automation-friendly and agent-oriented, so that workflows can increasingly be operated through reliable tooling rather than manual effort.\nWe’re looking for a strong generalist with a systems mindset:\nsomeone who is comfortable working across infrastructure, backend systems, and tooling, and who has seen ML systems in practice.\n\nthis is not a pure ML Engineer role. We’re especially interested in people who think deeply about reliability, scalability, performance, and resource efficiency in complex production environments.\n\nThis is a hands-on IC role with significant ownership. You’ll help shape how our ML platform evolves as we scale the number of models, workloads, tools and teams relying on it.\nWhat you’ll do\nDesign and improve the platform systems that support model training, evaluation, and production serving.\n\nBuild infrastructure and tooling that make ML workloads more reliable, scalable, and cost-efficient.\n\nDevelop internal tools and workflows that are easy to operate both by humans and by agents.\n\nWork on the architecture behind how models are deployed, served, and operated across research and product environments.\n\nImprove how we schedule, monitor, and debug workloads running on GPUs and cloud infrastructure.\n\nDevelop internal tools and abstractions and agentic systems that reduce operational overhead for researchers and engineers.\n\nDrive improvements across observability, automation, reliability, and developer experience.\n\nCollaborate closely with researchers and product engineers to understand pain points and turn them into robust platform capabilities.\n\nContribute to technical direction and make pragmatic architectural tradeoffs as the platform grows.\n\nYou’ll thrive in this role if you have\nStrong experience building or operating production systems with a focus on reliability, scalability, and maintainability.\n\nA systems mindset: you naturally think in terms of bottlenecks, failure modes, interfaces, resource usage, and long-term operability.\n\nSolid hands-on experience with cloud infrastructure, Linux, and infrastructure automation.\n\nExperience with Kubernetes and operating distributed workloads in production.\n\nStrong coding skills, ideally in Python or similar languages used for backend systems and tooling.\n\nStrong judgment around where automation adds leverage, and where human control and reliability matter most.\n\nExperience building internal platforms, developer tooling, or infrastructure abstractions used by other engineers.\n\nComfort working in ambiguous environments and taking ownership of open-ended technical problems.\n\nA pragmatic approach: you care about solving the right problem well, not over-engineering.\n\nParticularly relevant experience\nOperating ML infrastructure or model serving systems in production.\n\nSupporting research or data-intensive workloads.\n\nWorking with GPU-based systems or other performance-sensitive infrastructure.\n\nExperience with observability and debugging in distributed systems.\n\nFamiliarity with Terraform, Datadog, GitHub Actions, or similar tools.\n\nBonus points for\nExperience building agentic or LLM-powered internal tools.\n\nExperience with workflow orchestration systems such as Temporal.\n\nExperience working at the boundary between research and production engineering.\n\nFamiliarity with performance optimization, scheduling, or resource allocation problems.\n\nExperience building lightweight product or developer-facing 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