What we need to see: 5+ years of experience in machine learning engineering, AI engineering, software engineering, platform engineering, or a comparable production-focused role. Bachelors degree A solid history of advancing innovative AI or machine learning systems from prototype to production. Extensive knowledge in one or more fields including classical machine learning, computer vision, NLP, generative AI, or LLM applications. Strong system-design skills, including experience with distributed systems, data-intensive applications, and cloud infrastructure. Practical understanding of production LLM inference, including latency and efficiency trade-offs, context windows, token usage, model selection, and cost management. Experience working with containers, orchestration platforms, CI/CD, monitoring, observability, and production incident investigation. Sound engineering judgment around scalability, reliability, security, maintainability, and operational complexity. The ability to independently guide complex technical projects and make effective decisions in ambiguous environments. Strong communication and collaboration skills, including the ability to explain technical trade-offs to engineers, product teams, customers, and other collaborators. Ways to stand out from the crowd: Experience working with both traditional machine learning systems and contemporary LLM or agentic applications. Excellent judgment about when agent-based approaches are appropriate—and when a simpler solution is more effective. Experience making AI behavior measurable, observable, explainable, and safe in production. Experience optimizing inference systems for performance, infrastructure efficiency, and operating cost. A history of guiding engineers or heading cross-departmental technical projects. We are looking for engineers who care deeply about technical quality. They take ownership from building through production. They are motivated by the challenge of turning advanced
