Build and operate machine-learning training and dataset infrastructure for autonomous perception systems. Responsibilities include multi-sensor data indexing, developer tools and SDKs, frontier-model applications for data curation and synthetic data, training-job and evaluation tracking, and model versioning. The role requires strong Python, distributed training experience with TensorFlow or PyTorch, and Kubernetes-based workflow orchestration. Rust, experiment tracking, and production-scale model deployment are additional advantages.
Type: Direct hire, full-time · Base: $166,000–$220,000 + equity · Clearance: U.S. Person required; Top Secret eligibility desirable
VDS is recruiting an ML infrastructure engineer to build the training and data platform behind autonomous perception systems used across a defense-technology company.
What you'll do
- Build and run training and dataset infrastructure, including multi-sensor data indexing.
- Improve tools and SDKs used by perception engineers.
- Use frontier models for data curation, labeling, synthetic data and search.
- Track training jobs, evaluations and model versions.
- Partner with engineering teams in each business line.
What you bring
- Strong modern Python; some Rust (PyO3) is a plus.
- Distributed training in TensorFlow or PyTorch.
- Kubernetes and workflow orchestration (Flyte, Airflow or Dagster).
Nice to have
- Experiment tracking with Weights & Biases or MLflow.
Type: Direct hire, full-time · Base: [confirm range] + equity · Clearance: [confirm; U.S. Person likely]
VDS is recruiting a senior ML engineer to build core machine-learning capabilities shared across a defense-technology company's autonomy products.
What you'll do
- Develop and ship models used across multiple product lines.
- Build reusable ML components, pipelines and evaluation tools.
- Work with autonomy, perception and platform teams.
What you bring
- [confirm years] years of production ML engineering.
- Python and PyTorch or TensorFlow; experience deploying models at scale.
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