Member of Technical Staff, Research

As a Member of Technical Staff, Research at Augment Code, you will be responsible for driving product improvements from ideation to implementation. This involves identifying weaknesses in the product experience, building research infrastructure, creating data pipelines, designing and conducting machine learning experiments, and implementing production improvements. Your role will be integral in enhancing tools that developers use daily, making a tangible difference in their work.

To be considered for the Member of Technical Staff, Research position at Augment Code, candidates should have a strong background in software engineering and excellent problem-solving skills. A high degree of agency and the willingness to tackle unfamiliar problems are essential. Ideally, candidates will also have some experience with machine learning engineering and developing developer tools, although this is not mandatory. Most importantly, a passion for improving developer tools is a must.

Augment Code fosters a flexible and trusting work culture where independent thinking is encouraged. As a Member of Technical Staff, Research, you will enjoy flexible work hours, opportunities for personal growth, and a focus on results rather than hours spent at a desk. The company values ownership and provides an inclusive environment where you can collaborate with others while making impactful contributions.

As a Member of Technical Staff, Research at Augment Code, you'll receive a comprehensive benefits package that includes competitive salary and equity options, health, dental, vision, and life insurance, short and long-term disability coverage, and unlimited paid time off. The company also provides a tools stipend to ensure you have the resources needed for your role and hosts numerous social events to foster team connections.

Augment Code is committed to diversity and inclusion in the hiring process for the Member of Technical Staff, Research role and beyond. The company is an Equal Employment Opportunity employer, pledging to not discriminate based on race, religion, national origin, gender, sexual orientation, age, or any other legally protected characteristic. We aim to create an environment that fairly assesses and celebrates diverse talents.

When answering this question, it's essential to provide specific examples from past projects. Discuss the types of machine learning models you've worked on, the tools and frameworks you've used, and how your contributions brought value. Highlight your problem-solving skills and your ability to create and run effective experiments.

Discuss your systematic approach to identifying product weaknesses, such as user feedback collection, performance metrics analysis, or A/B testing. Emphasizing your analytical skills and your method for prioritizing improvements will resonate well with interviewers.

Share a specific challenge related to software engineering or machine learning. Focus on the steps you took to analyze the problem, the solution you implemented, and the outcome. Highlight your technical skills and your ability to work under pressure.

Talk about the tools and practices you use to maintain communication and collaboration, such as regular meetings, project management tools, and fostering an open-door policy for ideas and feedback. Specific examples of successful teamwork are particularly impactful.

Explain the methodologies you follow when setting up research infrastructure, including your experience with data pipelines and computational environments. Showcase your organizational skills and the importance of creating scalable systems to facilitate research.

Share your personal connection to software development and the joy of creating tools that facilitate better workflows for developers. Express your passion for solving challenges faced by users and how that drives your work.

Discuss your strategies for keeping up-to-date with industry trends, such as following relevant publications, attending conferences, or participating in online courses. Your proactive approach to learning reflects a commitment to personal growth and innovation.

Talk about the critical nature of experimentation in research, especially in machine learning. Describe how iterative testing helps improve models and contributes to product development, showing that you understand the balance between theory and practical application.

Choose a project that highlights your technical skills and teamwork. Focus on your specific contributions, the challenges faced, and the successful outcomes, illustrating your capacity to deliver effective solutions in real-world scenarios.

Discuss your method for task prioritization, such as using frameworks like the Eisenhower Matrix to identify urgent versus important tasks. Share how you balance competing priorities while maintaining high-quality deliverables.