Machine Learning Researcher PhD Intern

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. What You'll Do • 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 You're a Fit If You • 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 Bonus Points • Experience with CUDA, kernels, Triton, TensorRT-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 Why Morph • 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 Apply • 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 ML algorithms