Senior Machine Learning Engineer
- Boston, MA, United States
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Job details
- Experience
- 5+ years of professional experience in Machine Learning and Computer Vision.
- Posting language
- English
- Posted
Original job description
Senior Machine Learning Engineer – Robotics & Computer Vision
An innovative robotics company is seeking a Senior Machine Learning Engineer to help transform the future of warehouse automation through advanced AI and computer vision. This full-time opportunity focuses on developing and deploying next-Generation perception systems that enable robots to understand, interpret, and interact with complex physical environments. You'll work with cutting-edge technologies including Python, C++, PyTorch, YOLO, Mask R-CNN, Transformers, PointNet, TensorRT, ONNX Runtime, CUDA, and NVIDIA Jetson Orin to build high-performance 2D/3D vision models and deploy them directly onto robotic hardware.
Required Skills & Experience
- 5+ years of professional experience in Machine Learning and Computer Vision.
- Proven experience developing and deploying production ML solutions into physical-world applications such as robotics, autonomous vehicles, or IoT.
- Expert-level proficiency in Python and PyTorch.
- Working knowledge of C++ for model deployment, performance optimization, and system integration.
- Strong experience with 2D computer vision architectures, including YOLO, Mask R-CNN, and Vision Transformers.
- Experience with 3D computer vision, including PointNet, grasp generation, multi-view geometry, and camera calibration.
- Experience developing multimodal vision systems that combine 2D image inputs with 3D geometric information.
- Hands-on experience optimizing and deploying models using TensorRT, ONNX Runtime, CUDA, or similar technologies.
- Understanding of model quantization, graph optimization, and low-latency inference.
- Experience working with large-scale labeled datasets, including data curation, quality assurance, and identifying dataset bias.
- Experience developing and maintaining ML data pipelines and improving model performance through systematic evaluation.
- Ability to translate product requirements into engineering solutions and communicate technical trade-offs to non-technical stakeholders.
Desired Skills & Experience
- Experience deploying machine learning models on NVIDIA edge computing platforms, particularly NVIDIA Jetson Orin.
- Familiarity with Docker and containerized deployment environments.
- Experience with AWS or GCP, including services such as S3 and EC2.
- Familiarity with active learning strategies and automated dataset improvement workflows.
- Experience with data labeling platforms and annotation quality management.
- Familiarity with experiment tracking and ML lifecycle management tools.
- Experience integrating perception outputs with robotic control systems.
- Background in warehouse automation, robotic manipulation, industrial robotics, or autonomous systems.
- Experience with grasp planning, object pose estimation, and complex scene understanding.
- Experience mentoring engineers, conducting code reviews, and contributing to technical roadmaps.
What You Will Be Doing Tech Breakdown
- Programming: Python, C++ Machine Learning: PyTorch
- 2D Computer Vision: YOLO, Mask R-CNN, Transformers
- 3D Computer Vision: PointNet, Multi-View Geometry, Grasp Generation
- Model Optimization: TensorRT, ONNX Runtime, CUDA
- Edge Computing: NVIDIA Jetson Orin
- Data Engineering: Dataset Curation, Active Learning, Data Quality Automation
- Cloud Infrastructure: AWS / GCP, S3, EC2
- Development & Deployment: Docker, ML Pipelines, Experiment Tracking
- Robotics: Perception Integration, Grasping, Scene Understanding
Daily Responsibilities
- Design, develop, and train advanced multimodal vision models that combine 2D image data with 3D geometry to solve complex robotic grasping and scene-understanding challenges.
- Architect and improve the company's perception stack to enable robots to operate reliably in dynamic and unpredictable warehouse environments.
- Own the end-to-end deployment of machine learning models, taking solutions from research and experimentation into production robotic hardware.
- Optimize model performance through graph optimization, quantization, and inference acceleration using TensorRT, ONNX Runtime, and CUDA.
- Deploy and integrate models onto NVIDIA Orin edge computing platforms, ensuring low-latency inference and efficient hardware utilization.
- Develop high-performance Python and C++ code to integrate perception outputs into the broader robotic control system.
- Own and improve existing labeled datasets and data pipelines, identifying bottlenecks and opportunities to improve data quality.
- Implement active learning strategies to identify challenging edge cases and systematically improve model robustness.
- Monitor dataset quality, detect statistical bias, and automate quality assurance throughout the ML pipeline.
- Conduct rigorous code reviews and establish best practices for production-grade machine learning software.
- Mentor junior engineers and contribute to the team's technical direction and long-term perception roadmap.
- Collaborate with robotics, software, and product teams to translate business requirements into scalable engineering solutions.
- Communicate technical decisions, model performance, and engineering trade-offs to cross-functional stakeholders.
Applicants must be currently authorized to work in the US on a full-time basis now and in the future.
Motion Recruitment Partners (MRP) is an Equal Opportunity Employer. All applicants must be currently authorized to work on a full-time basis in the country for which they are applying, and no sponsorship is currently available. Employment is subject to the successful completion of a pre-employment screening. Accommodation will be provided in all parts of the hiring process as required under MRP’s Employment Accommodation policy. Applicants need to make their needs known in advance.
More about this job
Skills
- Machine learning
- Python
- C++
- PyTorch
- R
- ML
- Robotics
- ICT system integration
- Quality assurance
- Product comprehension
- Docker
- AWS
- Google Cloud
- Quality assurance procedures
- Control systems
- Estimating
- Coaching
- Data engineering
- Research