Machine Learning Engineer designing GPU computing kernels to optimize 3D GenAI models at Meshy. Collaborating with researchers to enhance performance and efficiency in GPU module development.
About the role
- Graduate Machine Learning Engineer working alongside experts to develop AI and ML solutions for Defence and National Security challenges. Collaborating with multidisciplinary teams to implement machine learning approaches.
Responsibilities
- Work closely with colleagues across Science and Engineering, and Product teams to help develop, test and implement ML algorithms that solve complex, real-world problems efficiently and at scale
- Apply established machine learning techniques and libraries to real-world datasets, with support and mentorship from senior team members
- Follow best practices in scientific experimentation, validation, and documentation
- Contribute to technical documentation, internal notes, and project reports
- Attend client meetings to understand customer needs and how solutions are delivered
- Take part in knowledge sharing, training, and professional development activities, including attending relevant events or conferences where appropriate to stay current with emerging ML technologies and techniques and support innovation within the team
Requirements
- A degree (or expected degree) in Computer Science, Applied Mathematics, Statistics, Physics, or a related STEM field
- Familiarity with modern machine learning libraries (e.g. PyTorch or TensorFlow) through coursework, projects, internships or extra-curricular activity
- Experience programming in Python in an academic or project-based context
- An interest in building practical systems that help users understand and benefit from machine learning models
- A strong foundation in scientific thinking, with an appreciation for experimental rigour and validation
- A willingness to learn, ask questions, and work collaboratively as part of a team
- An interest in working alongside our clients to understand and solve their complex problems
Benefits
- Hybrid working (some roles may require full time onsite attendance)
- Flexible hours
- Professional and personal development
- 25 days of annual leave (plus Bank Holidays and a company-wide break over Christmas)
- Salary Sacrifice Pension scheme with a 5% employer contribution (minimum 5% employee contribution)
- Private Healthcare (including dental and optical cover)
- Group Life Cover at three times your annual salary once you pass your probation period
- Enhanced Parental and Sickness Leave
- Home Office Setup Allowance
- Workplace Nursery Scheme
- Pet friendly office - A lot of our team bring their dogs to work!
Job title
Job type
Experience level
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Not specified
Degree requirement
Tech skills
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