Principal Machine Learning Engineer - Graph ML & Code Intelligence

Who We’re Looking For:
Do you see source code as a living graph and get fired up about turning billions of edges into actionable insight? At IR Labs you’ll be the founding Machine Learning Engineer for Graph ML & Code Intelligence. You’ll join a tight, cross functional squad of ML, compiler, and platform experts to build graph native models that untangle the world’s most complex software systems, then ship them to production in weeks, not quarters. Your mandate is truly end to end: design the graph learning roadmap, stand up high throughput pipelines, fuse GNNs with LLM stacks, and watch your models drive 10× impact for Fortune scale customers.

What You’ll Do:

• Own the graph-ML roadmap end-to-end: turn research into production, balance SOTA with real-world constraints, and champion graph learning across teams.
• Design and train modern GNNs/graph transformers; explore self-supervision, sparsity, and pretraining to lift retrieval, grounding, and reasoning.
• Build high-performance training/inference pipelines on distributed GPUs with efficient sampling, mixed precision, and custom optimization where needed.
• Fuse graphs with language systems to power retrieval and reasoning primitives across the product.
• Model complex technical artifacts as graphs (e.g., code/IR or telemetry) and learn over them for analysis and optimization signals.
• Ship low-latency, scalable graph services and APIs with streaming updates and robust SLAs.
• Benchmark and harden sparse+dense kernels; instrument for performance, correctness, and reliability.
• Establish ML/DataOps for large graphs (versioning, lineage, CI/CD) and embed security, privacy, and compliance by design; mentor and uplevel the team.

What You Bring to the Table:

• 8+ years delivering production ML; 5+ years leading large-scale graph learning in production (100M–B+ edges).
• Deep mastery of GNNs/geometric DL, graph theory, and practical graph querying.
• Proven impact combining graphs with LLM/NLP (e.g., KG-augmented retrieval, entity linking, grounding).
• Experience deriving graphs from complex sources (such as code/IR) for analysis, optimization, or security use cases.
• Strong systems chops: C++/CUDA or equivalent; fluency in GPU/distributed training and performance tuning.
• Track record building reliable pipelines and operating large data/feature stores for graph workloads.
• Operational excellence in orchestration, containerization, observability, drift detection, and automated retraining.
• Clear, persuasive technical leadership and mentoring across both technical and non-technical stakeholders.