Staff Gen AI Research Scientist
Listed on 2026-08-22
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Software Development
AI Engineer (Applied/Software), Machine Learning/ ML Engineer
Staff Gen AI Research Scientist
Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century's most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril's family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center.
As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years.
We are seeking an AI Research Scientist to serve as a founding ML expert on our team. In this role, you will design, fine-tune, and deploy the next generation of generative AI, LLMs, and agentic systems that power our air-dominance platforms and collaborative autonomous behaviors.
This is a highly applied research role (split roughly 60% applied research/experimentation and 40% hands-on coding) focused on making state-of-the-art LLM models smaller, faster, and smarter. You will work on both offboard systems (for complex mission planning, modeling, and simulation) and onboard systems—optimizing models to run directly on power- and compute-constrained edge hardware. As an early member of this initiative, you will have significant autonomy to set the technical direction, design our data collection strategy across test sites and simulations, and directly influence how multi-agent autonomy is deployed in critical missions.
WhatYou'll Do
- Build & Fine-Tune Core Models:
Develop, pre-train, and fine-tune in-house LLMs and multimodal foundation models. Apply SOTA post-training alignment techniques (SFT, RLHF, DPO) to maximize capability while minimizing cost and footprint. - Deploy at the Edge:
Architect and optimize models to run directly on tactical edge compute and power-constrained hardware onboard physical assets. Optimize model latency, memory usage, and execution speed through quantization, distillation, and pruning. - Develop Agentic Workflows:
Design and implement robust agentic architectures, multi-agent coordination frameworks, and planning loops for complex, multi-domain military missions. - Drive Multimodal Sensor Integration:
Collaborate closely with computer vision, perception, and motion planning teams to build systems capable of reasoning over diverse modalities, including camera feeds, radar, telemetry, and text-based operational orders. - Shape Data & Evaluation Strategies:
Define and execute data collection strategies across physical assets, test sites, and virtual simulations. Work with AI Infrastructure engineers to build scalable evaluation frameworks that measure model performance, reliability, and safety in high-stakes environments. - Rapid Prototyping to Production:
Build early-stage prototypes alongside customers, quickly iterate on feedback, and scale those prototypes into production-grade features deployed across our family of systems.
- Core Engineering Strength:
Strong production-level coding skills in Python and deep learning frameworks (like PyTorch or JAX). - ML & LLM Expertise:
Hands-on experience training, fine-tuning, and evaluating LLMs, Generative AI, or multimodal models. - Solid Foundation: A strong background in a classical technical discipline (Computer Vision, NLP, Robotics, or Speech) with 2+ years of dedicated experience focusing on generative models and modern transformer architectures.
- Tooling Familiarity:
Experience using modern model training, alignment, and orchestration tools (e.g., Axolotl, Hugging Face, Deep Speed, Megatron-LM, Lang Chain, or llamaIndex). - Problem-Solving & Adaptability:
Ability to operate comfortably in a fast-paced environment, moving from ambiguous mission requirements to concrete code and functional prototypes. - Academic/Industry Background:
Degree (B.S., M.S., or Ph.D.) in Computer Science, Machine Learning, Robotics, Physics, Mathematics, or a related technical field. - Clearance…
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