Software Engineer, Machine Learning Infrastructure

Requirements

• BSc, MSc, or PhD in Computer Science or equivalent
• 3+ years of industry experience in software engineering
• Strong backend engineering fundamentals, especially in Python and distributed systems
• Experience building production services, APIs, data pipelines, or ML infrastructure at scale
• Experience operating systems in production, including observability, debugging, reliability, incident response, and performance/cost optimization
• Hands-on experience with LLM inference and/or fine-tuning of open-weight models in production
• Ability to work across ambiguous, fast-moving technical areas and turn customer use cases into reusable platform capabilities
• Proficiency in using AI coding tools in the full software development lifecycle

Nice To Haves

• Experience with LLM inference engines and serving frameworks in production
• Experience with distributed/multi-node fine-tuning and training pipelines
• GPU performance work, such as multi-node/distributed inference and optimization
• Experience with Kubernetes, cloud infrastructure, GPUs, serverless/elastic GPU platforms, or high-throughput batch systems
• Experience with LLM gateways, model routing, vendor abstraction, or cost attribution
• Experience building developer platforms, internal platforms, or self-serve infrastructure
• Experience building and deploying AI agents or MCP servers in production
• Experience with eval systems, LLM observability, tracing, or vector databases

What You'll Be Doing

• Build infrastructure to move GenAI ideas from prototype to production
• Design scalable, high-performance systems for model serving and inference engines
• Partner with ML engineers, product engineers, and data scientists to develop GenAI capabilities
• Shape the future of the centralized GenAI platform

Perks and Benefits

• Opportunity to work on cutting-edge technology in a fast-paced environment
• Collaborate with talented teams from top tech companies
• Contribute to the future of AI-powered products and automation
• Supportive work environment promoting diversity, equity, and inclusion