Senior/Staff Machine Learning Engineer
@ DexteritySenior/Staff Machine Learning Engineer
This job is still taking applications, but it's been up a while.
About the job
Dexterity builds robots that transform logistics with full-stack systems. We focus on warehouse automation, creating AI-powered robots that handle tasks with skill and awareness. Backed by top investors, we are a diverse team dedicated to innovation and impact.
Requirements
- 5+ years in applying machine learning
- Strong Python and PyTorch skills
- Experience with ML pipelines and deployment
- Ability to optimize model performance
- Collaborative and team-oriented
Qualifications
- Degree in CS, EE, or Math
- Experience with cloud infrastructure
- Knowledge of Linux, Git, CI/CD
- Proven system design and maintenance ability
Full job description
Responsibilities
- Design and implement machine learning solutions across Dexterity’s robotics stack, including but not limited to perception, decision-making, action scoring, and predictive modeling
- Own the full ML development cycle for these solutions: data curation, labeling, training, evaluation, deployment, and iteration
- Build performant training and inference pipelines using PyTorch, with production-readiness and scalability in mind
- Collaborate closely with robotics, data platform, and simulation teams to integrate ML into real-time, latency-sensitive robotic systems
- Use profiling, monitoring, and experiments to optimize model performance and reliability
- Ensure reproducibility, traceability, and modularity across training and serving pipelines
- Maintain clean, production-quality code in Python (and C++ where required)
- Help establish best practices for model versioning, dataset management, and ML operations
Required Skills
- Degree in Computer Science, Electrical Engineering, or Mathematics 5+ years of industry experience applying machine learning to real-world, production systemsStrong Python skills and deep experience with PyTorch
- Ability to work fluently across ML tasks, e.g., classification, regression, ranking, segmentation, and structured prediction
- Strong engineering background with experience profiling, debugging, and optimizing model and pipeline performance
- Proven ability to design and maintain reliable systems, from model training to field deployment
- Experience with cloud-based infrastructure (AWS, GCP, Azure) and containerized environments (Docker)
- Familiarity with Linux, Git, CI/CD) and software development best practices (unit/acceptance/integration testing, code reviews)
Nice to haves
- Prior experience in robotics, autonomous systems, or real-time ML applications
- Exposure to multimodal data (e.g., RGBD, force-torque, pose estimates, telemetry)
- Experience deploying models using serving stacks like NVIDIA Triton, TorchServe, or custom low-latency frameworks
- Background in computer vision, geometric learning, or time-series modeling
- Experience with Kubernetes, Ray or other distributed training and inference systems
- Previous startup experience or experience in fast-paced, cross-disciplinary environments
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