Role overview
Machine Learning Engineer - Graph ML & Code Intelligence |
Denver, CO (Remote & Flexible)
An innovative lab that builds AI-powered developer tools designed to autonomously understand, analyze, and repair complex software systems are looking for a Machine Learning Engineer to join their team.
What you'll work on
- Own the graph-ML roadmap end-to-end by designing and training modern GNNs and graph transformers, modeling complex technical artifacts as graphs (code, IR, telemetry), and exploring self-supervision, sparsity, and pretraining to enhance retrieval, grounding, and reasoning capabilities
- Fuse graphs with language systems to power retrieval and reasoning primitives, combining graph learning with LLMs/NLP for KG-augmented retrieval, entity linking, and grounding across the product stack
- Build high-performance production infrastructure with distributed GPU training/inference pipelines, low-latency scalable graph services and APIs, efficient sampling and mixed precision optimization, and robust ML/DataOps for large graphs (100M–B+ edges) with versioning, lineage, and CI/CD
- Ensure reliability and performance by benchmarking and hardening sparse+dense kernels, instrumenting for correctness and observability, embedding security, privacy, and compliance by design, and mentoring the team on graph learning best practices
What we're looking for
- 8+ years delivering production ML
- Proven impact combining graphs with LLM/NLP
- Experience deriving graphs from complex sources (code, IR, telemetry) for analysis, optimization, or security use cases
- Deep mastery of GNNs, geometric deep learning, graph theory, and practical graph querying
- Strong systems expertise: C++/CUDA or equivalent; fluency in GPU/distributed training and performance tuning
Tags & focus areas
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