Builds and debugs AI agent infrastructure for healthcare automation, including prompt engineering, runtime issue tracing, evaluation datasets, simulation tooling, data pipelines, and observability dashboards. Requires 2-7 years experience with production LLMs/AI agents and TypeScript proficiency.
140k – 200k
Remote2+ YOEML Engineering
About the role
What You’ll Do
Work across our AI agent platform — writing prompts, debugging runtime issues, building agent simulation tooling, creating evals, interfacing with client data, and helping monitor system behavior at scale.
Trace and fix runtime bugs, then write regression tests.
Design evaluation datasets to simulate realistic workflows or red-team our system.
Build internal tooling for QA and agent simulation.
Normalize and transform messy client data for system integration.
Set up automatic testing and latency tracking infrastructure.
Create dashboards and observability tooling for agentic system behavior.
Expand on our existing eval & testing framework and agent simulation infrastructure.
Skills Required
Technical Skills
Proficiency in TypeScript
Strong generalist software engineering
Strong debugging skills (trace runtime failures, dig through logs, pinpoint issues in async or multi-step agent systems)
Data transformation and ingestion (build pipelines to normalize and convert unstructured data for AI systems)
Strong understanding of system design, including distributed systems and reliability/performance tradeoffs
Experience using modern AI coding tools (e.g. Cursor, GitHub Copilot, Claude)
Excellent documentation and testing discipline
Proficiency with Git
Soft Skills
Care about improving agent behavior
High agency; thrive with minimal structure
Comfortable getting in the weeds with details, edge cases, editing prompts, writing evals
Comfortable with ambiguity; work well with loose specs spanning prompts, code, RLHF
Learn fast and move fast; pattern-match from past systems work to LLM edge cases
Experience & Who Should Apply
2-7 years of experience working closely with LLMs or AI agents in production systems
Created internal tools or frameworks for QA, evals, or agent simulation
Contributed to fast-paced product cycles involving AI behavior, latency, user experience
Nice to Have
Experience with multi-agent systems, TTS/NLP pipelines, or structured output validation
Familiarity with testing frameworks, LangChain-style agent orchestration, or in-house eval harnesses
Experience with prompt engineering, LLM evals, and agent orchestration
Skills
TypeScriptLLMsAI AgentsGitCursorGithub CopilotClaudeLangChainPrompt EngineeringLlm EvalsAgent OrchestrationDistributed Systems
Build and own production AI agent systems (harnesses, evals, orchestration) on frontier LLMs for industrial supply chain workflows at Traba. Requires 1+ years shipping LLM/agent features to production, strong Python/TS skills, and high-agency in ambiguous customer environments.
140k – 200k
On-site1+ YOEML Engineering
Forward Deployed Engineer, RL Environments
LabelboxSan Francisco, CA
Builds and maintains sandboxed, reproducible RL environments for AI agent training, including terminal, browser, and tool-augmented setups. Requires 2+ years Python/systems engineering, containerization, and RL concepts understanding.
140k – 200k
Hybrid2+ YOEML Engineering
Machine Learning Engineer, Search Quality
GleanUnited States
Build advanced search quality systems using machine learning, including personalization signals, ranking models, and domain-adapted LLMs for enterprise search. Requires 2+ years experience in ML, search/NLP, strong coding in Python/Go/Java/C++, and bachelor's in CS/math.
140k – 265k
Hybrid2+ YOEML Engineering
Machine Learning Engineer II, Computer Vision Applied Science
PinterestSan Francisco, CA
Build and fine-tune vision-centric VLMs and generative models using Pinterest's visual-text datasets. Requires 2+ years industry computer vision experience and an M.S. or Ph.D.
139k – 286k
Remote2+ YOEML Engineering
Machine Learning Engineer, Core Engineering
PinterestSan Francisco, CA +2
Build and improve machine learning models for Pinterest's recommendation systems across Homefeed, Ads, Search, and more. Requires 2+ years experience in ML methods like personalization and recommender systems, plus hands-on work with large-scale data pipelines.