The primary focus of a Machine Learning Engineer is to ensure our technology remains at the cutting edge of the machine learning industry. With the support of the Intellegens Science team, you will identify and implement improvements to our current algorithms and technologies to ensure that we retain our place as market-leading developers of real-world machine learning.
This will involve both researching upcoming new technologies, and also understanding customer needs for new solutions and refinements to our existing approaches. You will collaborate with the team in optimising and realising these new improvements to our approach. There will also be the opportunity to collaborate with customers and external collaborators on specific research projects.
Main duties and responsibilities
Working with the team to develop new algorithms and apply them alongside our existing technology to customer problems with some of the world’s largest organisations. You will also work closely with Intellegens’ software engineers to guarantee new tools and approaches are robust and scalable, capable of being applied to benefit all our customers.
You will ensure that we remain at the cutting edge of machine learning through engagement with the academic and industrial machine learning community, with freedom to propose and implement new approaches to augment our existing technology. You will also support our Machine Learning Scientists with the best ways to apply machine learning solutions to customer problems, including occasionally supporting video calls or in-person meetings with customers.
What makes you our next Machine Learning Engineer?
- Degree in mathematics, statistics, computer science, machine learning, or similar field
- Interest and experience in statistical analysis and machine learning
- Highly motivated and adaptable to rapidly developing technical and commercial drivers
- Ability to clearly communicate both algorithms and applications with technical and business teams
- Familiar with python and common libraries (numpy, pandas, scipy)
- Material science, chemical science, or life science interest
- Good familiarity with one of the common AI packages (tensorflow/keras/pytorch)
- Application of machine learning to real-world problems
- Experience developing novel machine learning approaches
- Experience in customer-facing roles
- Experience with high-performance computing including fortran/C++
The above is not an exhaustive list and you are required to be flexible in your approach to carrying out your duties which may change from time to time in order to reflect business needs or the company’s continuous improvement. Intellegens is committed to providing training and development opportunities as part of the role to grow the technical, communication, and scientific skills of the successful candidate.
What can we offer you?
- A competitive financial package – salary and share options
- 5 weeks annual leave pro rata, flexible leave policy
- Salary sacrifice pension, with company savings being paid into the scheme
- A collaborative work environment with neither red tape nor bureaucracy
- Scope for career development as an early team member
- Support and resources to develop your skills and succeed in the role
- Hybrid working arrangements and a great team culture
- Access to an EAP, wellbeing champion, and financial advice
- Enhanced sickness policy
- Regular social and team building events
- Cycle scheme
At Intellegens we enable our customers to apply advanced machine learning methods, accelerating innovation in materials, chemicals, manufacturing, and beyond. Our software can extract hidden value from real-world experimental and process data, serving organisations such as NASA, Johnson Matthey, Boeing, and AstraZeneca. The results are: faster R&D; greener, more cost-effective, better products; and more efficient use of resources. Originally a spin-out from the University of Cambridge, we are at an exciting phase in our development, with well-established products and customers and ambitious growth plans including a move to new headquarters in Cambridge and expanding our team into the US. We push the boundaries of machine learning, while working on interesting projects in a friendly, informal, and motivational work environment.