At a Glance
- Tasks: Define quality standards for ML systems and automate QA workflows using Python.
- Company: Join Autodesk, a global leader in design software with a hybrid-first culture.
- Benefits: Flexible work options, competitive salary, and opportunities for professional growth.
- Other info: Collaborate with global teams and contribute to innovative projects in a dynamic environment.
- Why this job: Make an impact by ensuring the reliability of cutting-edge ML technologies.
- Qualifications: 7+ years in software engineering or QA for ML/AI systems and strong Python skills.
The predicted salary is between 60000 - 80000 £ per year.
As a Senior Machine Learning QA Engineer in the Research Enablement team, you will work side-by-side with researchers, Machine Learning Engineers and software engineers to define and uphold quality standards for ML systems. You are a quality-focused engineer who is passionate about reliable, repeatable evaluation of ML models and data. Your skills span test strategy, automation, and a little MLOps, with a strong software engineering base. You are excited to collaborate across research and product to ship ML capabilities with clear quality gates. You are comfortable working at the intersection of research and product and are competent in using Autodesk CAD software.
Reporting Structure: You will report to an Engineering Manager in Research Enablement.
Location: United Kingdom – we are a global team located in London, San Francisco, Toronto, and remotely. Autodesk is a hybrid-first company, allowing workers to work remotely, in an office, or a mix of both.
Responsibilities
- Define ML quality strategy and acceptance criteria across data, model, and system levels
- Design and maintain model evaluation suites, metrics, and test datasets
- Evaluating CAD RL model outputs for geometric validity or policy stability
- Defining structured rubrics that translate qualitative findings into measurable evaluation gates
- Testing ML Models from product side API Testing
- Automate ML QA workflows using Python and CI/CD (e.g., GitHub Actions, Jenkins)
- Create and maintain test harnesses for ML services and APIs
- Mentor teams on ML QA best practices and consistent evaluation standards
- Build quality gates for training and deployment pipelines (e.g., regression checks, drift detection)
- Contribute to multi-team projects and codebases, ensuring code quality and consistency
- Participate in code reviews and provide constructive feedback to peers
- Document and present findings and ideas across the company
Minimum Qualifications
- Bachelor’s degree in Computer Science, Engineering, or equivalent experience
- 7+ years of professional experience in software engineering or QA for ML/AI systems
- Strong programming skills in Python, with experience in test automation
- Familiarity with popular CAD environments tooling
- Proficient in Automation and UAT test suite/framework
- Experience designing QA frameworks or platforms used by multiple teams
- Excellent problem-solving skills and attention to detail
- Strong communication and collaboration skills
- Understanding of software architecture and design patterns
- Ability to work in an agile development environment
Preferred Qualifications
- Experience with data validation tooling (e.g., Great Expectations) or labeling workflows
- Familiarity with ML frameworks (e.g., PyTorch, TensorFlow)
- Experience with CI/CD tools and processes
- Experience with data pipelines and orchestration tools (e.g., Airflow, Metaflow)
- Familiarity with MLOps practices (model monitoring, drift, deployment checks)
- Experience with ML evaluation methods, metrics, and benchmarking
- Passion for learning new technologies and improving existing systems
- Experience with cloud providers (e.g., AWS, Azure, Google Cloud Platform)
- Experience testing ML services in production environments
- Knowledge of experiment tracking tools (e.g., Comet, MLflow, Weights & Biases)
Ideal Candidate
- You demonstrate initiative to provide solutions and to learn and develop new technologies
- Comfortable building QA systems from scratch and writing maintainable automation
- You enjoy learning and collaborating across global locations
- You are comfortable working in newly forming ambiguous areas
- You are comfortable building scalable and maintainable systems that will be relied on by others
- You can communicate well with others
Senior Machine Learning Test Engineer United Kingdom employer: Autodesk
At Autodesk, we pride ourselves on being an excellent employer that fosters a collaborative and innovative work culture. As a Senior Machine Learning Test Engineer, you will have the opportunity to work alongside talented professionals in a hybrid-first environment, allowing for flexibility between remote and office work. We are committed to employee growth, offering mentorship and continuous learning opportunities, making it an ideal place for those looking to make a meaningful impact in the field of machine learning.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Machine Learning Test Engineer United Kingdom
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those working at companies you're interested in. A friendly chat can open doors and give you insights that job descriptions just can't.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your projects, especially those related to ML testing and automation. This gives potential employers a taste of what you can do beyond your CV.
✨Tip Number 3
Prepare for interviews by practising common technical questions and scenarios related to ML QA. We recommend doing mock interviews with friends or using online platforms to get comfortable with the format.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining our team!
We think you need these skills to ace Senior Machine Learning Test Engineer United Kingdom
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the Senior Machine Learning Test Engineer role. Highlight your programming skills in Python, experience with test automation, and any familiarity with CAD environments. We want to see how you fit into our team!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about quality assurance in ML systems and how your background aligns with our needs. Don’t forget to mention your collaborative spirit and problem-solving skills – we love that!
Showcase Your Projects:If you've worked on relevant projects, whether in a professional setting or as personal endeavours, make sure to include them. Describe your role, the technologies you used, and the impact of your work. This helps us see your hands-on experience in action!
Apply Through Our Website:We encourage you to apply directly through our website for the best chance of getting noticed. It’s straightforward and ensures your application goes straight to the right people. Plus, we can’t wait to see what you bring to the table!
How to prepare for a job interview at Autodesk
✨Know Your ML Basics
Brush up on your machine learning fundamentals, especially around model evaluation and testing. Be ready to discuss how you would define quality standards for ML systems and share examples from your past experiences.
✨Showcase Your Automation Skills
Prepare to talk about your experience with test automation, particularly in Python. Highlight any CI/CD tools you've used, like GitHub Actions or Jenkins, and be ready to explain how you've automated QA workflows in previous roles.
✨Familiarise Yourself with CAD Tools
Since the role involves working with Autodesk CAD software, make sure you understand its basics. If you have experience evaluating CAD RL model outputs, be prepared to discuss specific challenges and how you addressed them.
✨Collaboration is Key
This position requires working closely with researchers and engineers. Think of examples where you've successfully collaborated across teams, and be ready to discuss how you can contribute to a multi-team project environment.
