Machine Learning Engineer

About the role

As a Machine Learning Engineer in Delivery, you will work closely with our simulation engineers, data scientists, and customers to understand and define the engineering and physics challenges we are solving. You will iterate with customers and use your influence to drive decisions around reliable deployment with measurable outcomes.

Responsibilities

• Work closely with our simulation engineers, data scientists and customers to develop an understanding of the physics and engineering challenges we are solving
• Design, build and test data pipelines for machine learning that are reliable, scalable and easily deployable
• Explore and manipulate 3D point cloud & mesh data
• Own the delivery of technical workstreams
• Create analytics environments and resources in the cloud or on premise, spanning data engineering and science
• Identify the best libraries, frameworks and tools for a given task, make product design decisions to set us up for success
• Work at the intersection of data science and software engineering to translate the results of our R&D and projects into re-usable libraries, tooling and products
• Continuously apply and improve engineering best practices and standards and coach your colleagues in their adoption

Qualifications

• Experience applying Machine learning methods (including 3D graph/point cloud deep learning methods) to real-world engineering applications, with a focus on driving measurable impact in industry settings
• Experience in ML/Computational statistics/Modelling use-cases in industrial settings (for example supply chain optimisation or manufacturing processes) is encouraged
• A track record of scoping and delivering projects in a customer facing role
• 2+ years’ experience in a data-driven role, with exposure to software engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps)
• Building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., TensorFlow, MLFlow)
• Distributed computing frameworks (e.g., Spark, Dask)
• Cloud platforms (e.g., AWS, Azure, GCP)
• Containerization and orchestration (Docker, Kubernetes)
• Strong problem-solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly
• Excellent collaboration and communication skills - with teams and customers alike
• A background in Physics, Engineering, or equivalent

Benefits

• Salary Range: $150,000 - $190,000 depending on experience
• Seniority will be assessed throughout our interview process

Pay

$150,000 - $190,000 depending on experience

Schedule

Hybrid based in the San Francisco area