Staff Gen AI Research Scientist

ABOUT THE TEAM 

The Air Dominance & Strike team at Anduril develops aerial and multi-domain robotic systems. The team is responsible for taking products like Fury (unmanned fighter jet) and Barracuda (air-breathing cruise missile) from concept to product. The team also develops Lattice for Mission Autonomy, Anduril’s premier software platform that enables masses of Fury, Barracuda, and other first and third party robots to collaborate across various missions. We work in close coordination with specialist teams like Perception, Motion Planning, Hardware, and Test Engineering to solve some of the hardest problems facing our customers. We are looking for AI research scientists, Applied scientists and roboticists excited about creating a powerful autonomy software stack that includes computer vision, motion planning, SLAM, controls, estimation, and secure communications. 

ABOUT THE JOB 

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. 

WHAT YOU’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. 

REQUIRED QUALIFICATIONS 

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, DeepSpeed, Megatron-LM, LangChain, 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 Eligibility: Eligible to obtain and maintain an active U.S. Top Secret security clearance. 

PREFERRED QUALIFICATIONS 

Edge Deployment Experience: Proven track record of compiling and running deep learning models on edge accelerators (e.g., NVIDIA Jetson, custom TPUs/ASICs) under severe power and compute constraints. 

Advanced Alignment Experience: Hands-on experience implementing Reinforcement Learning from Human/AI Feedback (RLHF/RLAIF) or direct preference optimization (DPO) loops. 

Multimodal Experience: Experience working with multimodal architectures (VLM, video-to-text, or sensor fusion) and diffusion models. 

AI-Powered Products at Scale: Experience building and shipping AI-powered products used by millions of users, with a deep understanding of the end-to-end lifecycle from research prototype to production deployment. 

Applying AI to Novel Domains: Track record of successfully applying AI/ML technologies to novel problems and domains where established approaches do not yet exist, demonstrating creativity and first-principles thinking. 

Continuous Learning & Field Awareness: Proven ability to rapidly learn new technologies and stay current with the fast-evolving AI landscape—regularly engaging with the latest research, tools, and techniques through publications, conferences, and the open-source community.