We are looking for a Senior ML/MLOps Engineer 🚀 #vacancy #job #ml #mlops #python #remote #ai #sre #platform

ℹ️ About the Company & Project: A global leader in AI video generation, trusted by over 90% of Fortune 100 companies to transform how teams communicate and create content. As a high-growth Series E unicorn (valued at $4B+ with over $500M raised from premier investors including Accel, Kleiner Perkins, and Nvidia's VC arm), are pushing the boundaries of generative AI.

💼 Responsibilities: — Design and improve platform systems for model training, evaluation, and production serving — Build robust infrastructure and tooling to make ML workloads scalable, reliable, and cost-efficient — Architect the deployment and serving of ML models across research and production environments — Improve scheduling, monitoring, and debugging for GPU and cloud-based workloads — Develop internal abstractions, developer tools, and agentic systems to reduce operational overhead — Drive continuous improvements across observability, automation, reliability, and developer experience (DX) — Collaborate closely with ML researchers and product engineers to turn pain points into robust platform capabilities — Contribute to technical direction and make pragmatic architectural trade-offs

🧠 Requirements: — Strong experience building and operating complex, high-load production systems — Deep systems mindset: ability to analyze bottlenecks, failure modes, and resource usage — Solid hands-on experience with Linux, cloud infrastructure, and infrastructure automation — Extensive experience with Kubernetes (K8s) and operating distributed workloads in production — Strong coding skills in Python (or similar) for backend systems and tooling — Proven experience building internal platforms, infrastructure abstractions, or developer tools — Pragmatic approach to problem-solving with a focus on reliability without over-engineering — Strong ownership and comfort working in ambiguous environments — English — B2+

💫 Nice to have: — Direct experience operating ML infrastructure, GPU clusters, or model serving systems in production — Familiarity with workflow orchestration systems (e.g., Temporal) — Experience building LLM-powered or agentic internal tools — Strong background in observability and debugging distributed systems (Datadog, Prometheus, etc.) — Hands-on experience with Terraform, GitHub Actions, and CI/CD pipelines — Experience bridging the gap between research and production engineering