The Opportunity

We're building out our AI Engineering Team. This is a chance to join at the earliest stage of a company where the engineering problems are genuinely hard - planning under uncertainty, multi-agent coordination, simulation, reinforcement learning - and the work is deployed in environments where the output actually matters.

You will work across the full ML lifecycle - transforming messy, real-world data into production-grade models deployed in high-stakes environments. You'll ship fast, work directly with the founders, and have outsized influence on the technical direction of the company.

Key Responsibilities

AI/ML Development: Design, train, fine-tune, and deploy ML models to solve real-world operational problems. Own the lifecycle from data exploration and feature engineering to evaluation and production monitoring.

Data & Systems Engineering: Build scalable data pipelines for structured and unstructured data (text, imagery, geospatial, sensor data). Develop reliable training and inference systems optimized for performance and edge deployment.

Engineering Excellence: Integrate models into robust, scalable production systems with strong testing, observability, and CI/CD practices.

Research & Experimentation: Prototype and benchmark new modeling approaches (LLMs, multimodal systems) to improve performance, robustness, and mission impact.

What You Bring

Required

• Bachelor's degree (B.Sc.) in Computer Science
• Strong software engineering fundamentals
• Solid understanding of machine learning principles (model evaluation, optimization, bias/variance tradeoffs)
• Hands-on experience with ML frameworks (PyTorch, TensorFlow, Hugging Face)
• Experience working with real-world datasets - cleaning, feature engineering, experimentation, and performance analysis
• Strong communication and collaboration skills
• Willingness to travel up to 15% to engage with DoW partners
• Must be eligible to obtain and maintain a U.S. security clearance

Preferred

• Master's degree (M.Sc.) in Computer Science, Machine Learning, or related field
• Experience with edge-deployable or offline ML systems
• Experience optimizing models for latency and compute constraints
• Familiarity with distributed training or large-scale data processing
• Exposure to geospatial, multimodal, or reasoning systems
• Experience with containerization and orchestration tools

Benefits

• Competitive salary and equity
• Opportunity to help build a category-defining defense tech company
• Full health benefits
• 401(k)
• Offices in Washington DC and San Francisco