Senior Software Engineer (ML Infrastructure)

• The Roku Entertainment Assistant team builds conversational AI experiences used across millions of Roku devices. The team works across fulfilment ranking, model delivery, offline and online evaluation, low-latency services, observability and product quality. Its published work includes shared model-serving and MLOps paths, automated evaluation and retraining, caching and telemetry, and agent-assisted release and operational workflows
• We are looking for a Senior Software Engineer with strong machine-learning infrastructure experience to help the Roku Entertainment Assistant team deliver reliable, high-quality conversational experiences at scale. You will own production systems across ranking, model delivery, evaluation and LLM-agent behaviour, working closely with machine-learning, product, data and platform partners
• This is a hands-on engineering role for someone who can move comfortably between software architecture, ML lifecycle decisions and production operations. You will improve the paths the team uses to train, evaluate, deploy and observe models, while helping us manage latency, quality and cost as the product grows
• Own fulfilment-ranking pipelines from training orchestration and quality gates through deployment and online validation
• Build and operate an offline-evaluation platform, combining LLM-as-judge harnesses with deterministic checks for answer quality
• Develop the Roku Entertainment Assistant’s LLM agent, including tool routing, retrieval, guardrails and answer caching
• Design caching as an intentional latency and cost lever for high-volume services
• Build observability for ML and LLM systems, including latency attribution, quality metrics, tracing and per-request cost
• Improve the reliability and operability of distributed systems, and lead the diagnosis of difficult cross-service failures
• Drive accountable AI-assisted engineering practices across the team, building on the agents already used in release workflows
• Work across machine-learning, product, data and platform boundaries to turn ambiguous problems into measurable delivery outcomes

Benefits

• Medical, wellness and financial benefits
• Free snacks and access to the company fitness center
• Unlimited paid time off policy
• Work from home opportunities- Experience with AWS or GCP, Kubernetes, SQL warehouse and batch-processing technologies such as Trino, Presto or Spark, Kafka or other streaming systems, and Go or Python would be useful. Node.js experience is a plus. We value transferable evidence over an exact match to every named technology
• Experience with caching systems and the trade-offs involved in using caching for low-latency services
• Strong production software-engineering experience, including designing, testing, operating and debugging distributed services
• A commitment to automation, CI/CD, code quality and evidence-led engineering decisions
• Experience designing for latency, resilience, observability and cost in production systems
• A collaborative approach and the ability to explain technical trade-offs to partners across disciplines
• A degree in Computer Science, Electrical Engineering or a related field, or equivalent practical experience
• A practical understanding of the ML lifecycle: training, evaluation, versioning, deployment, online validation, monitoring and safe rollback
• Experience with LLMs, embeddings, semantic search, retrieval or agent systems, including how you evaluate quality and control failure modes
• Meaningful ownership of ML infrastructure or production model delivery, with clear evidence of your contribution and its operational or adoption impact