Senior Software Engineer, ML Infrastructure Roku, Inc.

Coding Black Females Ltd; Cambridge; Remote; GBP 90,000 - 130,000; Full time

Job summary

Roku is seeking a Senior Software Engineer with strong ML infrastructure experience to own production systems across ranking, model delivery, evaluation and LLM-agent behaviour. You will improve training, evaluation, deployment and observation paths while managing latency, quality, and cost as the product scales.

You will work across ML, product, data and platform teams to turn ambiguous problems into measurable delivery outcomes, building on agent frameworks, routing, and caching to support

Qualifications

• Strong production software-engineering experience
• Ownership of ML infrastructure or production model delivery
• Understanding of ML lifecycle: training, evaluation, deployment, monitoring
• Experience with latency, observability, and cost in production systems
• Collaborative, able to explain trade-offs to partners

Responsibilities

• Own fulfilment-ranking pipelines from training orchestration through deployment and online validation
• Build and operate an offline-evaluation platform with LLm-based checks
• Develop the Roku Entertainment Assistant’s LLM agent including tool routing and caching
• Design caching to reduce latency and cost for high-volume services
• Build observability for ML/LLM systems including latency attribution and cost tracing
• Improve reliability and operability of distributed systems and diagnose cross-service failures
• Drive accountable AI-assisted engineering practices across the team
• Collaborate across ML, product, data and platform to turn ambiguous problems into measurable outcomes

Job description

About the team

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.

What is the role?

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.

How will I use AI at Roku?

At Roku, we don’t just use AI, we work with it. AI agents and smart tools help power drafts, analysis, and repetitive workflows, while our people bring direction, judgment, and accountability. We’re looking for curious, adaptable builders who can show how they’ve used AI or automation to move faster, raise the bar, and scale their impact.

We value your AI skills if you have built fluency across the agentic engineering toolchain — coding harnesses like Claude Code or Cursor, MCP servers, custom skills, or agent frameworks. And you can describe projects where you shipped real work with these tools. You know how to drive an agent, verify its output, and ramp on an unfamiliar codebase with an agent helping you.

What will you be doing?

• 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.

We’re excited if you have

• Strong production software-engineering experience, including designing, testing, operating and debugging distributed services.
• Meaningful ownership of ML infrastructure or production model delivery, with clear evidence of your contribution and its operational or adoption impact.
• A practical understanding of the ML lifecycle: training, evaluation, versioning, deployment, online validation, monitoring and safe rollback.
• Experience designing for latency, resilience, observability and cost in production systems.
• Experience with LLMs, embeddings, semantic search, retrieval or agent systems, including how you evaluate quality and control failure modes.
• Experience with caching systems and the trade-offs involved in using caching for low-latency services.
• A commitment to automation, CI/CD, code quality and evidence-led engineering decisions.
• 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.
• 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.