- This is the modeling half of manufacturing data science at Hadrian
- The factory turns geometry into parts: a CAD model, a material, a set of tolerances, a route through stations
- This role predicts what that process will do before it runs, and gets better at it with every part that goes through
- Our factories generate rich process data on high-mix, low-volume aerospace parts, but most parts are near-unique, so the classic “lots of history per SKU” playbook doesn’t apply
- The leverage is representation: embed a part by its geometry, material, tolerances, and route, then predict cycle time, cost, tool wear, quality, and triage risk from the parts like it, before the first chip is cut
- The work spans forecasting and prediction (cycle time, tool life, quality and yield, demand, queue and lead time, always with calibrated uncertainty), representation learning (part and operation embeddings so a part with no history inherits the behavior of its neighbors), and geometric modeling (features and models straight off CAD, mesh, and point cloud)
- Deep models where they earn their keep, classical where it wins
- Those predictions feed quoting, scheduling, capacity, and DFM, and you’ll own the pipelines that serve them, partnering with ML Platform to deploy and Data Engineering on features
- Build and ship production models for cycle time, tool life, quality, and demand, using calibrated uncertainty (quantile, conformal, or Bayesian) rather than point estimates alone
- Model directly off geometry by engineering features and building geometric/graph models that predict cycle time, cost, DFM and tolerance risk, and triage probability
- Build a part and operation embedding layer that represents a part by geometry, material, tolerances, and route, retrieves similar parts, and transfers their behavior to cold-start new ones
- Validate honestly through backtesting that respects time ordering and part-family leakage, and make a defensible case for deep versus classical methods on each problem
- Own models end to end on the platform, including reproducible training, serving, monitoring, and retraining, in partnership with ML Platform and Data Engineering
- Close the loop in production by detecting drift and quality anomalies so predictions improve as new data lands
- Turn predictions into decisions for quoting, scheduling, capacity, and DFM; design experiments and A/B tests to measure real impact, then document and hand off to operations
Benefits
- 100% coverage of platinum medical, dental, vision, and life insurance plans for employees
401k - Relocation stipend if you’re moving from outside of LA
- Flexible vacation policy
Works with limited, high-value data and knows how to borrow strengthValidation done right: backtesting, leakage control (time and part-family), calibrationPython; turns a messy process into features and a model into a decision an operator or a downstream system can consumeStrong classical ML and statistics (GBMs, Bayesian/hierarchical, survival, causal)Deploys and monitors models; thinks about pipelines and drift from the start, not afterDeep learning that ships (PyTorch), and the judgment to know when not to use itRepresentation learning and embeddings; similarity and retrieval; transfer/few-shot for sparse dataForecasting and prediction on real, messy manufacturing data, with honest uncertaintyGeometric deep learning: mesh / point-cloud networks, GNNs, PyTorch GeometricCAD / B-rep, feature recognition, and turning part geometry into ML featuresRetrieval and ANN at scale; embedding storesBayesian and hierarchical modeling for small data; physics-informed MLSurvival and reliability modeling (tool life, degradation)Aerospace or precision-manufacturing background; DFM intuitionDigital twins and simulation; causal inference; sensor / IoT data #J-18808-Ljbffr