Top Machine Learning Engineer Jobs

Optimize and deploy computer vision, signal processing, and ML algorithms to GPU-enabled embedded platforms. Profile CPU/GPU performance, reduce latency, containerize solutions, work in Linux, and collaborate with ML engineers to transition models into production on low SWaP systems.

Develop and deploy deep learning computer-vision systems: source and curate data, design and train models (object detection), implement production-ready PyTorch/Python software, integrate with AWS, apply CI/CD and testing, and collaborate on research and technical solutions for intelligence-focused applications.

Develop backend systems and ML pipelines for computer-vision applications using Python, Docker, Kubernetes and AWS. Build RESTful APIs and streaming services, optimize performance, follow SDLC best practices, and collaborate in an Agile team to deploy containerized deep learning models.

Design, train, and deploy manipulation policies (imitation learning, RL, behavior cloning) for deformable food handling. Build data collection pipelines, evaluation metrics, and recovery behaviors. Partner with perception and robotics teams to validate end-to-end grasp-to-place performance and iterate policies on physical robots in production.

The Senior Machine Learning Engineer will lead the perception stack, focusing on detection, tracking, classification, and sensor fusion for targeting systems, with responsibilities including model development and real-time optimization on embedded devices.

Build and improve AI/ML systems and data pipelines for a B2B SaaS platform; shape product and technical strategy; work with customers; mentor team members; ensure secure, robust, and scalable infrastructure.

As a Senior Software Engineer, you'll develop machine learning models and software applications, translating technical requirements into solutions while ensuring excellence in product delivery.