Machine Learning Engineer - Graph ML & Code Intelligence
Confirmed on the employer's own hiring board on Oct 4, 2026. First seen by Alion on Aug 2, 2026.
IR Labs is the innovation lab inside Integrated Research where small, cross-functional squads chase outsized, industry-defining opportunities. We operate like a funded startup-rapid sprints, bold experimentation, zero bureaucracy-backed by the global footprint and resources of a public company. Our charter is simple: turn cutting-edge AI research into products that customers can’t imagine working without. We target the hardest problems in software and then move fast to ship solutions that create 10x impact.
Our flagship is Agentic SQA - a software quality system that turns noisy signals (static analysis, fuzzers, sanitizers, CI output) into context-aware triage, validated actions, and developer-ready outputs. We’re in beta now, starting narrow on a focused C/C++ risk-analysis workflow targeting a specific bug class, and expanding from there into broader coverage, richer evidence, and stronger control layers. The stack combines LLVM/clang static analysis, a code knowledge graph, and agentic LLM workflows. The direction is bigger than code review: a systems verification platform that closes the gap where human-speed review is breaking down.
If you thrive on autonomy, crave world-class technical challenges, and want to see your ideas hit production quickly, IR Labs is your launch pad. Join us and help build the future-one breakthrough at a time.
Before you apply: Agentic SQA is live and free to use. Install and run it. Form a view. We’ll discuss that during the interviews.
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 IRLabs you’ll be the founding Machine Learning Engineer for GraphML & CodeIntelligence. 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.