Join a newly formed team building and deploying AI systems for use on tactical edge hardware. You will work with experts in AI, language models, and systems engineering to develop reliable solutions for demanding defense applications, including environments with limited computing resources.
Train and fine-tune language models for applications that need to run on edge devices.
Optimize model performance across GPU, CPU, and specialized accelerator platforms.
Design and implement multi-agent systems for different mission scenarios.
Connect AI agents with sensors, effectors, and uncrewed vehicle systems.
Benchmark and evaluate agent capabilities, performance, and reliability.
Build scalable, secure, and fail-safe systems for high-stakes environments.
Four to ten years of professional software engineering experience after graduation, including experience contributing to system design or architecture.
A bachelor's degree or higher in computer science or a quantitative field such as physics or mathematics.
Hands-on experience building LLM applications and agent workflows, including production systems, fine-tuning, agent optimization, or inference optimization.
Strong proficiency in Python, C++, and/or Rust, with experience using tools such as PyTorch, Hugging Face Transformers, LangChain, or vLLM.
Experience optimizing AI models and runtimes for edge hardware; familiarity with tools such as Ollama, Mojo, TVM, or TensorFlow Lite is useful.
A track record building technically demanding software with rigorous standards for reliability, security, performance, and testing. Defense, aerospace, robotics, applied research, or large-scale technology experience is relevant.
Experience working with customers or across functions, and willingness to travel up to 25%.
U.S. citizenship and eligibility for a U.S. security clearance are required.
Compensation is $145,000 to $275,000 annually. Visa sponsorship is not available.
Hybrid role based in Palo Alto, California.
Job details are sourced from the employer's original posting.
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