Job Title: Machine Learning Engineer
Reporting To: VP Data and AI
Location: Bristol - Hybrid 2+ days in office per week
Salary: £60,000 - £80,000 Depending on skills and experience
As a Machine Learning Engineer at Beam you’ll report to Cate Seale, our VP of Data and AI. You’ll be responsible for designing, developing, and deploying advanced ML models, primarily in the marine and renewable energy domains.
This role involves building robust, scalable, and high-performing ML systems, with an emphasis on computer vision, autonomy, and edge ML applications.
As a key contributor, you are responsible for innovation across the model lifecycle, from data preparation and rapid experimentation to deployment and monitoring.
In addition to model development, ML Engineers help optimize and enhance our MLOps infrastructure and CI/CD pipelines. Staying at the forefront of advancements in ML and MLOps is a vital aspect of your work, as you integrate emerging techniques and tools to maximise impact on business objectives.
You will make a significant impact by delivering efficient and accurate data processing and interpretation, enabling next-generation capabilities in autonomy and computer vision, and helping to establish Beam as a leader in sustainable marine and renewable technologies.

GROW TOGETHER WITH BEAM WHERE YOU MAY LEARN OR BUILD ON YOUR EXPERTISE IN THESE SKILLS:

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• Knowledge or experience with Autonomous Surface Vehicles and Autonomous Underwater Vehicles

• Deploying ML on edge devices
• Real-time inference and Edge ML
• Working with large language models
• Reinforcement learning
• Marine or geospatial domains
• AWS or other cloud platforms
• PyTorch, PyTorch Lightning, OpenCV, CVAT, Docker, ROS
• Edge computing frameworks like TensorRT

• Deploy and Manage ML Products – Maintain and optimize CI/CD pipelines, ensuring continuous integration and safe, reliable model releases.
• Monitor and Improve Model Performance – Proactively evaluate deployed models, identify issues, and implement enhancements.
• Drive Innovation and Experimentation – Stay updated with ML/AI advancements, prototype new concepts, and integrate emerging techniques.
• Hands-on ML Engineering – Develop model architectures, data pipelines, and leverage containerization/orchestration (e.g., Docker, Kubernetes).
• Ensure Code Quality and Best Practices – Conduct code reviews, advocate performance optimizations, and mentor team members
• Collaborate in Agile Environments – Work cross-functionally, participate in Agile workflows, and refine processes for better ML solution delivery