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Jobs(31,365)/AI Engineer Role(768)/TRM Labs (44) - AI Agent Engineer
TRM Labs

TRM Labs - AI Agent Engineer

San Fracisco , California , United States - Hybrid$200k - $275k+ Equity4mo ago
In OfficeNAArtificial IntelligenceAI EngineerPythonVectorObservable

Requirements

• Strong engineering background with deep experience in backend or systems work (Python preferred) • Hands-on experience building with LLMs, agents, and tooling frameworks (LangChain, semantic caches, vector DBs, etc.) • Comfort working with agentic pipelines and optimizing information flow into AI systems • Thoughtful approach to system design, with an eye for safety, scalability, and explainability • High product empathy - you care about how agents impact real users (analysts) and optimize accordingly • Bias toward experimentation and iteration - you’re excited to try, learn, and ship fast • Previous experience with knowledge graphs, task orchestration, or AI safety a plus • Work Location Requirement: This role is based in our San Francisco office. Employees in this position are expected to work from the office at least three (3) days per week to support in-person collaboration and team engagement. • Work Location Requirement: • The AI Engineering team operates with a high level of collaboration and interdependence, fostering an inclusive culture where connections extend beyond work. We value diverse personalities and seek someone who is both results-driven and sociable, with strong communication, leadership, and teamwork skills, along with a positive, solutions-oriented mindset.

Responsibilities

• Architect and implement a robust agentic framework that supports tool use, context retrieval, memory, and planning • Build intelligent, modular agents that automate investigative tasks and augment analyst decision-making • Extend and scale our LLM infrastructure (e.g. OpenAI, Anthropic, local models), including prompt engineering, RAG, and evaluation loops • Design safe, observable, and auditable agent behaviors — ensuring reliability in high-sensitivity environments • Evaluate performance across metrics like reasoning, latency, success rate, and hallucination, and iterate based on user feedback and system telemetry • Contribute to a culture of high ownership, rapid experimentation, and ethical AI deployment

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