Test ML Engineer

The responsibilities of a Test ML Engineer at Matterworks include designing, building, and deploying scalable machine learning models, collaborating with data scientists and software engineers, preprocessing data, evaluating model performance, and optimizing machine learning models for diverse environments. This role demands a keen understanding of ML frameworks and programming in Python to solve real-world problems.

To qualify for the Test ML Engineer position at Matterworks, candidates should have a strong knowledge of machine learning frameworks such as TensorFlow or PyTorch, proficiency in Python programming, and experience with cloud platforms. Ideal candidates are also curious and collaborative, ready to create actionable insights from complex data.

At Matterworks, the work environment is hybrid with a strong emphasis on in-person collaboration in Somerville. This setup allows team members to engage closely with colleagues across departments, fostering innovation. The culture values communication, creativity, and teamwork, making it a vibrant place to work.

Matterworks offers an attractive benefits package for Test ML Engineers that includes a competitive base salary, stock options, comprehensive health and dental coverage, retirement plans with company matching, flexible work, unlimited time away policy, commuter benefits, and support for continuing education and conference participation.

Matterworks is committed to the professional development of its employees, including Test ML Engineers. The company supports growth through continuous education opportunities, coursework reimbursement, and funding for conference participation, ensuring that team members stay at the forefront of advancements in the field of machine learning and biological data.

When responding, detail specific projects you've worked on that involved TensorFlow or PyTorch, emphasizing the techniques you used, the challenges you faced, and the outcomes achieved. Sharing examples of model design and training will demonstrate your hands-on experience and problem-solving skills.

Discuss your systematic approach to data preprocessing, including handling missing values, normalization, and feature selection. Highlight your experience with tools and libraries, and mention how this work lays the foundation for effective machine learning model training.

In your answer, mention various performance metrics like accuracy, precision, recall, and F1-score. Explain how you use these metrics in different contexts and the importance of cross-validation in ensuring your model generalizes well to unseen data.

Share a detailed story about a specific problem, what methods you employed, the data you used, and what insights were gained. Focus on the impact of your solution and any innovative approaches you took to overcome obstacles.

Explain your understanding of scalability in ML, including techniques like model simplification, the use of cloud platforms, or distributed computing. Provide specific examples where you've successfully scaled a solution in a previous role.

Discuss integration strategies like APIs, microservices, and continuous deployment. Emphasize any previous experiences where you successfully transitioned a model from development to production, highlighting collaboration with software engineers.

In your response, mention specific resources you use, such as online courses, conferences, articles, or professional networks. Highlight the importance of continuous learning in the rapidly shifting field of machine learning.

Emphasize the importance of collaboration in your work, especially working with data scientists and product teams. Share experiences that demonstrate your ability to communicate effectively and contribute within a team setting.

Provide an example where you simplified technical ideas, illustrating how you tailored your message based on the audience's understanding. This will showcase your communication skills and adaptability.

Highlight your enthusiasm for Matterworks' mission and culture. Emphasize how your values align with the company's goals and why you are excited about the opportunity to contribute to AI tools for biological data and therapeutic discovery.