Software Engineer, ML Research

As a Software Engineer focusing on ML Research at Windsurf, your responsibilities will include training and fine-tuning large language models, prioritizing high-value machine learning experiments, and transforming experimental outcomes into scalable features. You'll also participate in a collaborative ML reading group to enhance the team's expertise.

To be considered for the Software Engineer, ML Research role at Windsurf, candidates should possess strong software engineering skills, an understanding of AI and language model feasibility, and experience in training large production neural networks. Familiarity with AI-powered developer tools is a strong plus.

Windsurf fosters growth by encouraging independent project work, providing opportunities to engage in ML experiments, and facilitating participation in an ML reading group to deepen knowledge. Our collaborative environment promotes shared learning and continuous development.

Yes, at Windsurf, we believe our best work happens in person. The team collaborates five days a week at our office in downtown Mountain View, CA, which is conveniently located near public transit.

Recently, the Software Engineer, ML Research team at Windsurf has deployed an autocomplete product that supports hundreds of thousands of daily active users, developed fine-tuned models for our Plugin Command, and enhanced real-time context retrieval for better code suggestions.

When answering this question, provide specific examples of projects where you've trained large language models, the challenges you faced, and the outcomes. Highlight your problem-solving skills and understanding of model architecture.

Discuss your criteria for prioritizing experiments, including potential impact on product features and computational efficiency. Sharing actual experiences where this decision-making process influenced project outcomes will strengthen your answer.

Use the STAR method (Situation, Task, Action, Result) to structure your response, focusing on the steps taken post-experimentation to integrate findings into your product offerings, along with measurable results to showcase success.

Explain the concept of the feasibility frontier and its importance in AI application, emphasizing how you evaluate and explore unknowns in technology through experimentation, demonstrating your knowledge of current trends.

Detail your approach to validating model performance through testing and documentation. Mention specific metrics you track and how you incorporate feedback from experiments to refine and enhance your models.

Discuss strategies for managing uncertainty, such as setting incremental goals, utilizing agile methodologies, and regularly reassessing priorities based on ongoing results and new insights.

Mention specific tools you've utilized in previous projects, such as TensorFlow, PyTorch, or others, explaining why they are effective for your research and development processes.

Reflect on your approach to teamwork and communication, emphasizing collaborative environments where knowledge sharing fosters better outcomes and how you ensure all voices contribute to project success.

Use a specific example to illustrate the problem, your analytical process to diagnose the issue, and the solutions you implemented to resolve it. Conclude with positive outcomes and lessons learned.

Share your passion for AI and ML, detailing what excites you about the industry's future and how you hope to contribute to advancements in this area as part of the team at Windsurf.