mlops engineer

A global leader in AI video generation, trusted by over 90% of Fortune 100 companies to transform how teams communicate and create content, operates 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, and pushes the boundaries of generative AI.

• 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.

• 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;
• Upper-intermediate 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.

• No conditions specified