Machine Learning Researcher

Morph builds the fastest LLM code-editing inference engine in the world. We hit 10,500 tok/sec per request on NVIDIA hardware.

Our stack powers high-throughput AI workflows for vibe coding apps, devtools, PR bots, and IDEs.

We’re hiring a founding ML Researcher to push the limits of model capability, throughput, and reliability across inference, retrieval, and edit application. This is a research role that ships. If your work cannot survive contact with production, it does not count here.

We’re looking for someone with broad, T‑shaped spikey experience across research, systems, and product, plus a deep spike in modern LLM training and inference. You bring taste and judgment. AI can accelerate execution. It cannot replace those.

• Design and run experiments for LLMs specialized for code workflows: retrieval, search, editing, and tool use
• Train and fine‑tune models (SFT + preference / RL variants), build evals, and close the loop until results are real
• Turn new research into production: model packaging, serving constraints, latency budgets, failure modes, monitoring
• Work directly on inference performance when it matters: KV cache strategy, batching, quantization, speculative decoding, kernel level bottlenecks
• Collaborate on data strategy: high signal datasets, preference data formats, automatic labeling, and rigorous evaluation

• PhD level or equivalent experience with PyTorch (plus TF or JAX is fine)
• Can implement papers without cargo culting them, and can explain why they work. Understands how to distiguish between papers that are noise and real
• Have shipped ML systems that run under real constraints: latency, cost, reliability, observability
• Understand modern LLM training mechanics and tradeoffs (data, objectives, RL, evals, inference)
• Prefer ownership and agency over committees and process theater

• Experience with CUDA, kernels, Triton, Tensor
  
  RT-LLM, vLLM, or custom inference stacks
• Experience with retrieval systems (embeddings, reranking, indexing) and evaluation methodology
• You have strong opinions about what matters in ML, and can defend them with evidence

• Zero fluff. Work directly with the founder. Everyone on the team is an ML engineer
• No busywork. If it doesn’t move the needle, we don’t do it
• Work on the fastest coding subagents in the world, and the research that makes it faster and smarter

• Describe the ML project you’re most proud of. Go deep on modeling choices, training setup, data, evals, failure cases, and what you’d do differently - the founder reviews every application personally and is a former ML engineer
• Describe what you’re deeply obsessed with (anything). We care about intensity and taste