Applied Research Engineer

What You'll Do

• Explore novel approaches to memory, context, and context generation.
• Define the problem, run the experiments, ship the result.
• Own research to production end-to-end: dataset creation and curation, experiment design, evaluation, training and finetuning, and production deployment.
• Train, finetune, and evaluate models on Zep's domain.
• Build the eval harnesses that catch regressions before they ship.
• Work with our model serving stack to operate inference at low latency and reasonable cost on AWS.

What We're Looking For

• 6+ years of production engineering with a strong backend systems background.
• Master's in Computer Science or equivalent.
• Strong research skills: methodology, dataset creation and curation, experiment design, and evaluation.
• Hands-on experience with model finetuning.
• Working familiarity with transformer architectures, training and finetuning workflows, and evaluation.
• PyTorch and OpenAI Triton for experimentation.
• Working experience with model serving technologies: vLLM, SGLang, or Triton Inference Server.
• You've operated inference in production.
• Python, plus high proficiency in one of Rust, C++, or Go.
• You can work in critical-path code and on performance.
• Python-only is not enough.
• Hands-on AWS experience in production: deployments, monitoring, scaling, cost and reliability tradeoffs.

Nice to Have

• Published or open-source work in retrieval, memory systems, or LLM evaluation.

This Role Is Probably NOT a Fit

• If you're an ML researcher or model trainer who hasn't shipped research to production.
• Your background is primarily Python application work without lower-level systems experience.
• You haven't operated production backend systems with real latency or throughput requirements.