Twilio Machine Learning Engineer 2 at Twilio to build and scale pipelines converting raw conversation data into structured customer memory. Responsible for maintaining memory accuracy and quality measurement systems.
Responsibilities
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.
Qualification
Experience building backend servicesFluency with AI-assisted dev tools (eExperience with Go and/or PythonExperience building LLM-backed productsPractical experience applying MLThis role will be remote
Required
Twilio values diverse experiences from all kinds of industries, and we encourage everyone who meets the required qualifications to apply. If your career is just starting or hasn't followed a traditional path, don't let that stop you from considering Twilio. We are always looking for people who will bring something new to the table!
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.
We prioritize connection and opportunities to build relationships with our customers and each other. For this role, you may be required to travel occasionally to participate in project or team in-person meetings.
