Machine Learning Engineer 2

Twilio

7d•$128,560 - $170,000•Remote

About The Position

Twilio is hiring a Machine Learning Engineer for its Conversation Memory team, which builds the memory layer behind Twilio’s Conversational AI platform. The role focuses on building and scaling pipelines that turn raw conversation data into structured, searchable customer memory, along with evaluation systems that keep that memory accurate as the product evolves. The systems extract relevant information from conversations, associate it with the right customer, and retrieve it in milliseconds when needed. Because incorrect, stale, or misattributed memory can harm customer experiences, measuring quality is a central part of the work. This is a remote role, but it is not eligible for hiring in San Francisco, Oakland, San Jose, or the surrounding areas. Occasional travel may be required for in-person project or team meetings. Twilio is a remote-first company, though ad-hoc in-person attendance may be requested for team gatherings, functional off-sites, or customer meetings. Twilio offers competitive pay, generous time off, parental and wellness leave, healthcare, a retirement savings program, and other benefits that vary by location. The role may also be eligible for participation in Twilio’s equity and corporate bonus plans. The stated salary ranges apply to candidates in certain listed locations; compensation for candidates elsewhere will be discussed during hiring.

Requirements

Experience building backend services and demonstrated ability to learn new languages, frameworks, and tools as needed.
Ability and curiosity to investigate how systems behave in production, with a desire to learn to operate services rigorously and accountably.
Experience building or working with evaluation or quality-measurement systems, such as regression detection or quality metrics, or a desire to learn.
Grounding in observability practices, including structured logging, metrics, and using them to debug distributed systems in production.
Fluency with AI-assisted development tools, such as agents for code generation, review, and debugging, applied with deliberate direction and critical thinking.

Nice To Haves

Experience with Go and/or Python.
Experience with event-streaming systems such as Kafka.
Experience building LLM-backed products.
Practical experience applying machine learning, such as building, evaluating, or productionizing models.

Responsibilities

Build the extraction pipeline that converts raw data into structured memory, including tuning LLM prompts and schemas that determine what an AI agent remembers about a customer.
Own the retrieval path for answering what is known about a person in milliseconds, working with embeddings, vector search, and ranking.
Contribute to evaluation systems that measure whether memory is improving by creating benchmarks and regression gates that run before changes ship.
Diagnose production issues across ingestion, extraction, and indexing using logs, metrics, and traces.
Partner with Product and ML teams to translate customer problems into system changes and evaluation criteria.