Staff Machine Learning Engineer, Agent Memory & Reasoning (University)
RemoteUnited States
United StatesRemoteFull TimeSenior LevelSmall
Full TimeSenior LevelSmall
Job Summary
Build agent memory systems that generate, curate, refine, and store information while enforcing confidentiality and scoping constraints. Design reusable skills for better reasoning and decision-making, then run tests and benchmarks to validate improvements. Take undefined problems and design shippable solutions, documenting methodologies for team replication. Shape the technical roadmap for agent memory and reasoning as the new University team stands up. This role focuses on teaching agents to reason and remember without retraining models, avoiding fine-tuning or deep GPU work for the next six months.
Required Qualifications
- You've shipped production agents or agent adjacent systems at a company, not just in a lab.
- Experience with memory, context engineering, or techniques that make agents reason better without retraining them.
- An applied, builder's mindset: rigorous thinking, shipped in days and weeks, not semesters.
- Comfortable owning ambiguous, senior level problems on your own.
- Strong software engineering fundamentals to go with your ML and agent experience.
- Practical fluency with the modern agent tooling stack: vector databases (Pinecone, Weaviate, pgvector, or similar), retrieval frameworks (LangChain, LlamaIndex), and agent orchestration tools such as LangGraph.
- Comfortable working directly with LLM provider APIs (OpenAI, Anthropic, or similar) and embedding models for retrieval and memory systems.
- Experience with agent evaluation and benchmarking tooling (e.g. LangSmith, Ragas, TruLens, or a custom eval harness).
- Strong communicator, written and verbal.
Desired Qualifications
- An advanced degree (MS or PhD), paired with real industry experience.
- Experience testing and benchmarking agent behavior.
- Experience building 'skills' or reusable capabilities for AI agents.
- Experience with agents that handle serious volumes of complex information (think a genuinely capable assistant, not a demo).
- Time spent in a fast scaling product and engineering org.
- Experience with large data volumes
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