AI Engineer

Pytho AI•San Francisco, CA

About The Position

Prepare first. Decide first. Win first. At Pytho, we're on a mission to ensure warfighters are prepared for every challenge they face. We are committed to assembling a world-class team of exceptional problem solvers, combining expert engineering talent with deep experience in National Security. We build AI for the most important problem in defense — making sure the force is ready to fight. Our software is deployed and in use with DoW partners today. 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.

Requirements

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

Nice To Haves

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

Responsibilities

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.
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.
Integrate models into robust, scalable production systems with strong testing, observability, and CI/CD practices.
Prototype and benchmark new modeling approaches (LLMs, multimodal systems) to improve performance, robustness, and mission impact.