Data Scientist

Hadrian is building autonomous factories to reindustrialize America. By combining AI, advanced software, robotics, and full-stack manufacturing, we help aerospace and defense companies build rockets, satellites, aircraft, ships, and other mission-critical systems up to 10x faster and at significantly lower cost.

Following our $1.37B Series D at a $7.87B valuation, Hadrian is rapidly expanding our manufacturing footprint, launching new capabilities across welding, casting, forging, electronics, additive manufacturing, and more, while scaling our Factory-as-a-Service platform to transform how critical products are built.

Backed by leading investors including JPMorgan Chase, Valor Equity Partners, Andreessen Horowitz, Founders Fund, 137 Ventures, Lux Capital, T. Rowe Price, and Morgan Stanley, we’re building the future of American manufacturing—and looking for exceptional people to help make it happen.

If you’re ready to take on the most challenging and rewarding work of your career while helping create American manufacturing jobs for generations to come, you’re exactly who we’re looking for.

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.

• Forecasting and prediction on real, messy manufacturing data, with honest uncertainty.
• Representation learning and embeddings; similarity and retrieval; transfer/few-shot for sparse data.
• Deep learning that ships (PyTorch), and the judgment to know when not to use it.
• Strong classical ML and statistics (GBMs, Bayesian/hierarchical, survival, causal).
• Validation done right: backtesting, leakage control (time and part-family), calibration.
• Python; turns a messy process into features and a model into a decision an operator or a downstream system can consume.
• Deploys and monitors models; thinks about pipelines and drift from the start, not after.
• Works with limited, high-value data and knows how to borrow strength.

• Geometric deep learning: mesh / point-cloud networks, GNNs, PyTorch Geometric
• CAD / B-rep, feature recognition, and turning part geometry into ML features
• Retrieval and ANN at scale; embedding stores
• Bayesian and hierarchical modeling for small data; physics-informed ML
• Survival and reliability modeling (tool life, degradation)
• Aerospace or precision-manufacturing background; DFM intuition
• Digital twins and simulation; causal inference; sensor / IoT data

For this role, the target salary range is $170,000 – $300,000 (actual range may vary based on experience).

This is the lowest to highest salary we reasonably and in good faith believe we would pay for this role at the time of this posting. We may ultimately pay more or less than the posted range, and the range may be modified in the future. An employee's pay position within the salary range will be based on several factors, including, but not limited to, relevant education, qualifications, certifications, experience, skills, geographic location, performance, and business or organizational needs.

• Medical, dental, vision, and life insurance plans for employees
• 401k
• Relocation support may be provided for certain situations, based on business need.
• Flexible vacation policy
• Equity