Staff Engineer - Machine Learning

As a Staff Engineer - Machine Learning at Hudl, you will define the technical direction for the Applied Machine Learning team, own complex AI/ML projects, set the standards for engineering excellence, and drive innovation. Your role will include working alongside multiple business units to ensure that projects align with business goals and meet high-quality standards.

To qualify for the Staff Engineer - Machine Learning position at Hudl, you must have extensive experience in developing scalable AI/ML systems, a strong product focus demonstrated through a track record of impactful deliveries, and management experience to coach and influence engineering teams. Expertise in areas such as GPU acceleration, MLOps, and real-time systems is also essential.

Hudl provides a supportive work environment that champions work-life harmony and autonomy. As a Staff Engineer - Machine Learning, you'll have access to flexible working options, a culture that encourages career growth, and resources to support your wellbeing, including medical benefits and an Employee Assistance Program.

At Hudl, we believe in lifelong learning and provide numerous resources and opportunities for professional development tailored for the Staff Engineer - Machine Learning role. From training programs to access to industry events, we empower you to continue growing your skills and expertise.

While not a strict requirement, having prior experience in the sports industry is a nice-to-have for the Staff Engineer - Machine Learning position at Hudl. Familiarity with applying AI/ML in sports to generate insights can be a valuable asset in contributing to our mission.

When answering this question, highlight a specific project where you defined the technical direction, collaborated with a team, and delivered high-impact results. Emphasize the challenges you faced and how you executed strategies to overcome them.

Discuss your strategies for effective communication, such as regular check-ins, using visualization tools, and creating a transparent environment. Mention how these practices foster collaboration and understanding among stakeholders.

Explain the importance of monitoring in the AI/ML lifecycle, including performance metrics, data drift detection, and feedback loops. Give examples of tools and frameworks you have utilized to optimize model performance.

Describe how you lead by example, set clear expectations, and encourage knowledge sharing and skill-building. Highlight any initiatives you’ve implemented to foster continuous improvement and elevate team standards.

Provide specific examples of projects where you've leveraged GPU acceleration to enhance AI/ML efficiency. Discuss how this technology improved processing speed and model performance for your applications.

Share a story where you identified a gap or opportunity and took the initiative to introduce a new idea or process. Explain the steps you took to implement this innovation and the results that followed.

Explain how user feedback is vital in shaping AI/ML products to meet user needs. Detail your approach to collecting, analyzing, and integrating this feedback into development cycles.

Discuss your strategy for creating accessible documentation tailored to different stakeholders ranging from technical to non-technical audiences. Emphasize clarity, conciseness, and regular updates.

Talk about your experience in prioritization frameworks and how you assess project urgency and impact. Describe how you effectively allocate resources and communicate changes within the team.

Share your insights on the current challenges, such as data integrity, real-time analytics, or ethical considerations. Discuss how these can impact product development and your approach to resolving similar issues.