Develop and deploy state-of-the-art vision, VLM, and VLA models for autonomous perception systems. Requires 7+ years experience (with PhD), mastery of ML and computer vision, PyTorch/TensorFlow, TensorRT/ONNX, C++/Python, and ability to obtain SECRET clearance.
234k – 351k/yr
On-site7+ YOEML Engineering
About the role
What You'll Do
Design, train, fine-tune, and maintain state-of-the-art vision, vision-language, and vision-language-action models that improve perception and decision-making for autonomous systems.
Build scalable data pipelines, supervised fine-tuning (SFT) workflows, and evaluation loops that continuously improve model performance on mission-relevant tasks.
Deploy and optimize machine learning models for embedded hardware using technologies such as ONNX, TensorRT, and hardware-accelerated inference frameworks.
Apply modern machine learning techniques to solve challenging perception and autonomy problems across aerial and other autonomous systems operating in complex, real-world environments.
Translate cutting-edge machine learning research into production-ready capabilities by balancing model performance, robustness, computational efficiency, and operational reliability.
Partner closely with perception, autonomy, platform, and software engineering teams to integrate machine learning capabilities into mission-ready autonomous systems.
Develop benchmarks, testing methodologies, and evaluation frameworks to measure model performance, identify failure modes, and guide future improvements.
Improve training infrastructure, developer tooling, deployment workflows, and model lifecycle management to accelerate experimentation and production delivery.
Required Qualifications
Typically requires a minimum of 10 years of related experience with a Bachelor’s degree; or 9 years and a Master’s degree; or 7 years with a PhD; or equivalent work experience.
Mastery of machine learning fundamentals.
Experience training and deploying ML models for computer vision in a production setting.
Strong understanding of 3D vision problems/algorithms.
Experience with machine learning frameworks such as PyTorch and TensorFlow.
Demonstrated expertise in deploying models using TensorRT and ONNX.
Proficiency in C++ and Python.
Strong analytical and problem-solving skills, with the ability to translate research into practical applications.
Ability to obtain a SECRET clearance.
Preferred Qualifications
Experience with developing autonomous systems for defense customers.
Experience with training/finetuning vision-language models, vision-language-action models, and/or world models.
Contributions to open-source projects in machine learning or computer vision.
Track record of publications in leading computer vision and robotics conferences and journals (e.g., CVPR, ICCV/ECCV, RAL, ICRA).
Senior Staff Software Engineer, Autonomous Pilot Integration
Shield AIWashington, DC +3
Senior technical leader defining autonomy strategy and architecture for new platforms in space, maritime, and contested logistics. Owns end-to-end development and integration of mission behaviors, control, coordination, and executive autonomy from simulation through live tests; requires 10+ years experience and expert C++ skills.
234k – 351k/yrOn-site10+ YOEML Engineering
Senior Staff Software Engineer, Autonomous Pilot Integration
Shield AIWashington, DC +3
Senior technical leader defining autonomy architecture and integration strategy for launched effects platforms in multi-agent, often disconnected environments. Owns end-to-end development, fielding, and operational support of mission behaviors, platform control, and fleet-scale autonomy for defense customers.
234k – 351k/yrOn-site10+ YOEML Engineering
Senior Staff Software Engineer, Autonomous Pilot Integration
Shield AIWashington, DC +3
Own technical direction for Expeditionary autonomy portfolio on V-BAT and X-BAT unmanned aircraft platforms. Define long-term strategy, lead complex integration of mission behaviors, flight interfaces, payloads, and multi-agent operations while mentoring senior engineers and influencing cross-team architecture.
Technical leader on Datadog's APM team building and deploying GenAI/ML models for agentic investigations, automated troubleshooting, and incident triaging. Requires 10+ years experience leading large-scale GenAI initiatives end-to-end in product environments.
234k – 234k/yrHybrid10+ YOEML Engineering
Staff Software Engineer - ML Observability
DatadogBoston, MA +1
Lead development of LLM observability features at Datadog, building tools for monitoring, tracing, and evaluating AI systems in production. Requires expertise in distributed systems, GenAI applications, and model internals.