Machine Learning Engineer
Skills
ArtifactsCUDAContextDriftInfraredLarge Language ModelsOptimization
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- 8+ years delivering production ML
- 5+ years leading large-scale graph learning in production
- Deep mastery of GNNs geometric DL graph theory and practical graph querying
- Proven impact combining graphs with LLM NLP workflows
- Experience deriving graphs from complex sources such as code or IR
- Strong systems chops with C++ CUDA or equivalent
- Fluency in GPU distributed training and performance tuning
- Track record building reliable pipelines and large feature stores for graph workloads
- Operational excellence in orchestration containerization observability drift detection and automated retraining
- Clear persuasive technical leadership and mentoring skills
Nice to have
- Autonomous
- Cross-functional
- Innovative
- Systems-minded
- Detail-oriented
Day to day
- Own the graph-ML roadmap end-to-end and translate research into production-ready systems
- Design and train advanced GNNs and graph transformers to improve retrieval grounding and reasoning
- Build high-throughput distributed GPU pipelines and low-latency graph services with streaming updates