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Machine Learning Engineer - Computer Vision & Robotics

remoteCambridge, MA, USСкор 60.5/1001нед назад
Аналитика рынка
📊 AI / ML / DS: зарплаты и спрос на рынке
Стек
c++fine-tuninggitpythonpytorchtensorflow
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Описание
AI is redefining the future of autonomous intelligence. Spun out of MIT and backed by DoD contracts, we are building breakthrough AI and autonomy solutions for unmanned systems operating in GPS-denied and contested environments. We are a fast-growing, dual-use technology company at the forefront of national security and commercial innovation, with a mission to push the boundaries of what autonomous systems can achieve. AI means being part of a team shaping the next decade of autonomy from defense applications to commercial opportunities in areas like logistics, disaster response, and beyond. If you want to work at the cutting edge of AI/ML and robotics with a company that’s poised for major impact and growth, we want to hear from you. Responsibilities Design and optimize ML models for computer vision and robotics under tight compute and memory budgets. Build robust training and fine-tuning pipelines for large-scale datasets. Compress, accelerate, and deploy models (quantization, distillation) for embedded accelerators (NPU/TPU/GPU delegates). Profile and tune on-device inference pipelines for latency, throughput, and accuracy. Write clean, efficient, and maintainable code across Python and/or C++. Stay current on state-of-the-art ML techniques and efficient edge deployment research in autonomy and perception Requirements Bachelor’s or advanced degree in Computer Science, Engineering, or related field. Hands-on experience applying ML to computer vision and/or robotics. Proficiency with PyTorch or TensorFlow. Strong coding skills in Python or C++ (ideally both). Experience deploying ML models and building training pipelines. Familiarity with Git and collaborative software development practices. Nice to Have: CUDA kernel development and model optimization (quantization, pruning, distillation). Experience with ONNX, TensorRT, or OpenVINO for deployment. Robotics middleware (ROS2). SLAM, 3D perception, or sensor fusion (LiDAR, IMU). Rea
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