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- Tasks: Collaborate on innovative ML projects and build cutting-edge AI capabilities.
- Company: Join Autodesk, a leader in creative software solutions.
- Benefits: Competitive salary, bonuses, stock options, and comprehensive benefits.
- Other info: Dynamic, inclusive culture with opportunities for growth and collaboration.
- Why this job: Make a real impact in the world of design and technology.
- Qualifications: 3+ years in machine learning and strong software engineering skills required.
The predicted salary is between 60000 - 80000 £ per year.
The work we do at Autodesk touches nearly every person on the planet. By creating software tools for making buildings, machines, and even the latest movies, we influence and empower some of the most creative people in the world.
As a Machine Learning Engineer at Autodesk Research, you will work side-by-side with researchers and engineers to build and enable ML-powered capabilities in Autodesk products. You are an ML engineer with strong software engineering skills who is passionate about solving problems and building things, and excited to collaborate with AI researchers to implement generative AI features. You will operate in a fast-moving, research-driven environment where problems are often not fully defined, requiring strong problem framing and rapid teamwork in a hybrid working model from our offices in London.
Responsibilities- Collaborate on projects at the intersection of research and product with a diverse, global team
- Build and iterate on ML pipelines, prototypes, and evaluation workflows, enabling rapid deployment and testing of experimental models
- Work with complex datasets and representations for model training, evaluation, and analysis
- Analyze errors and provide solutions to problems
- Present results to collaborators and leadership
- BSc or MSc in Computer Science, or equivalent industry experience
- 3+ years of machine learning experience
- Experience working in research or experimental environments with evolving requirements
- Proficiency with modern deep learning libraries and frameworks (PyTorch, HuggingFace)
- Experience with computational geometry and 3D data (e.g., meshes, B-Rep models)
- Experience working with diverse data representations, including 2D and 3D geometry
- Experience with version control, reproducibility, and deploying machine learning models
- Experience using modern AI/GenAI tools in development or experimentation workflows
- Excellent written documentation skills
- Advanced degree (MSc/PhD) in Machine Learning, Computer Science, or related field
- Experience scaling machine learning training and data pipelines (e.g., using Ray or similar frameworks)
- Experience with cloud services and architectures (e.g. AWS, Azure)
- Experience with LLMs or generative AI systems (fine-tuning, prompting, evaluation)
- Knowledge of the design, manufacturing, AEC, or media & entertainment industries
- Experience with Autodesk or similar products (CAD, CAE, CAM, etc.)
Machine Learning Engineer 3D Geometry/ Multi-Modal in London employer: Autodesk, Inc.
Contact Detail:
Autodesk, Inc. Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer 3D Geometry/ Multi-Modal in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with Autodesk employees on LinkedIn. A friendly chat can sometimes lead to opportunities that aren’t even advertised!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those involving 3D geometry or multi-modal data. This will give you an edge and demonstrate your hands-on experience.
✨Tip Number 3
Prepare for technical interviews by brushing up on your problem-solving skills. Practice coding challenges and be ready to discuss your past projects in detail. We want to see how you think and approach problems!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re genuinely interested in joining the Autodesk team. Let’s make it happen!
We think you need these skills to ace Machine Learning Engineer 3D Geometry/ Multi-Modal in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Machine Learning Engineer role. Highlight your experience with ML pipelines, 3D geometry, and any relevant projects. We want to see how your skills align with what we do at Autodesk!
Showcase Your Projects: Include links to your GitHub or any projects that demonstrate your machine learning expertise. We love seeing practical applications of your skills, especially if they involve generative AI or complex datasets.
Craft a Compelling Cover Letter: Your cover letter should tell us why you're excited about this role and how you can contribute to our team. Share your passion for solving problems and collaborating with researchers – it’s what we value at Autodesk!
Apply Through Our Website: Don’t forget to apply through our official website! It’s the best way for us to receive your application and ensure it gets the attention it deserves. We can’t wait to hear from you!
How to prepare for a job interview at Autodesk, Inc.
✨Know Your ML Fundamentals
Brush up on your machine learning fundamentals, especially around deep learning libraries like PyTorch and HuggingFace. Be ready to discuss how you've applied these in past projects, particularly in 3D geometry or multi-modal contexts.
✨Showcase Your Problem-Solving Skills
Prepare to share specific examples of how you've tackled complex problems in research or experimental environments. Highlight your ability to frame problems and iterate quickly, as this is crucial for the role at Autodesk.
✨Familiarise Yourself with Autodesk Products
Get to know Autodesk's software tools and how they relate to machine learning. Understanding their applications in design, manufacturing, and media will help you connect your experience to their needs during the interview.
✨Prepare for Collaborative Discussions
Since collaboration is key in this role, think about how you've worked with diverse teams in the past. Be ready to discuss your communication style and how you present results to both technical and non-technical stakeholders.