Machine Learning Engineer 2

Hiring and how we work

See yourself at Twilio

Join the team as Twilio’s next Machine Learning Engineer, Customer Memory.

This position is needed to build and scale the pipelines that turn raw conversation data into structured, searchable customer memory, and the evaluation systems that keep that memory accurate as the product evolves.

You'll join the Conversation Memory team, which builds the memory layer behind Twilio's Conversational AI platform: the systems that extract what matters from millions of conversations, resolve it to the right customer, and retrieve it in milliseconds when an agent needs it. Memory that is wrong, stale, or crossed between customers is worse than no memory at all, so quality measurement is a first-class part of the work.

In this role, you’ll:

• Build the extraction pipeline that turns raw data into structured memory, tuning the LLM prompts and schemas that decide what an AI agent remembers about a customer and what it forgets.
• Own the retrieval path that answers "what do we know about this person?" in milliseconds, working with embeddings, vector search, and ranking
• Contribute to the eval systems that tell us whether memory is actually getting better, turning a fuzzy question of quality into benchmarks and regression gates that run before every change ships.
• Diagnose production issues across the ingestion, extraction, and indexing path using logs, metrics, and traces.
• Partner with Product and ML teams to translate customer problems into system changes and evaluation criteria.
• Experience building backend services, and a demonstrated ability to pick up new languages, frameworks, and tools as the problem demands.
• A curious approach to service management: ability to investigate how systems actually behave in production, and desire to continuously learn how to operate them with rigor and accountability.
• Experience building or working with evaluation/quality-measurement systems, such as regression detection or quality metrics, or the desire to learn.
• Solid grounding in observability practices: structured logging, metrics, and using them to debug distributed systems in production.
• Fluency with AI-assisted dev tools (e.g. agents for code generation, review, debugging), applied with deliberate direction and critical thinking.
• Experience with Go and/or Python.
• Experience with event-streaming systems (e.g. Kafka).
• Experience building LLM-backed products.
• Practical experience applying ML, e.g. building, evaluating, or productionizing models.
• This role will be remote, but is not eligible to be hired in San Francisco, CA, Oakland, CA, San Jose, CA, or the surrounding areas.

…