Senior Machine Learning Engineer

As a Senior Machine Learning Engineer at Polaron, you will be responsible for optimizing existing algorithms for material characterization and working on adapting machine learning methods specifically for materials science applications. Your role will involve a robust level of autonomy, where you'll influence the direction of our SaaS platform aimed at materials engineers.

To apply for the Senior Machine Learning Engineer position at Polaron, you should ideally have a degree in Computer Science, Engineering, AI, Math, Physics, or a similar field. A PhD in a STEM subject is desirable. Additionally, you need at least 2 years of experience in writing production-level code for computer vision applications using Python ML libraries like PyTorch and TensorFlow.

In the Senior Machine Learning Engineer role at Polaron, you'll be working with a diverse set of technologies, including Python/PyTorch for machine learning, GitHub for version control and CI/CD practices, as well as cloud platforms like AWS or Azure. Other technologies you might encounter include Docker, Kubernetes, Terraform, and PostgreSQL for database management.

At Polaron, we pride ourselves on a fantastic company culture that values innovation, collaboration, and personal growth. We are a supportive team dedicated to building a sustainable future, and we encourage open communication and the sharing of ideas among team members, fostering a creative and friendly environment.

Yes, Polaron offers a competitive benefits package for the Senior Machine Learning Engineer position. This includes a competitive salary, 25 holidays a year plus two festive leave days, gym memberships, flexible working policies, and opportunities for equity within our fast-growing company.

When discussing your machine learning project, focus on your role, the technologies you used, the problem you were trying to solve, and the results achieved. Highlight any specific challenges you faced and how you overcame them, showcasing your problem-solving abilities.

Your answer should reflect a systematic approach. Discuss techniques like hyperparameter tuning, feature selection, or ensemble methods. Provide examples where you've successfully improved algorithm performance, linking it to practical applications in materials science.

Discuss your hands-on experience with cloud platforms like AWS or Azure. Provide specific examples, such as deploying models, utilizing cloud storage, or resource management to scale applications. Highlight how cloud services have benefitted your projects.

Express your commitment to continuous learning through attending workshops, online courses, and reading recent publications in the field. Share any communities or forums you participate in, underscoring how these experiences keep you abreast of industry trends.

Demonstrate your communication skills by choosing a concept like 'neural networks' or 'overfitting' and breaking it down using simple analogies or examples. Showcase your ability to convey technical ideas to a diverse audience effectively.

Another important area to discuss is your experience with version control systems like Git. Explain how you've used Git for collaboration in projects, emphasizing workflows such as branching and merging, which are essential in a team environment.

Be honest and choose a real challenge. Outline the issue, your role in addressing it, and the steps taken for resolution. Emphasize collaboration, technical skills, and the learning outcomes from overcoming that challenge.

Discuss practices such as cross-validation, testing on varied datasets, or employing regularization techniques. Provide any relevant examples to show how you've validated model performance and ensured robustness in your projects.

Explain your approach to project management, whether it's Agile, Scrum, or another methodology. Describe how you adapt these methods to machine learning projects, focusing on iterative processes and feedback loops.

Express your enthusiasm for Polaron's mission and values. Highlight your passion for materials science and AI, along with the opportunity to work autonomously, contributing to meaningful innovations in sustainability and advanced materials.