Staff Machine Learning Engineer

As a Staff Machine Learning Engineer at Zocdoc, your main responsibilities include developing and enhancing our Search algorithms and ML models, collaborating with cross-functional teams to deliver impactful features, and mentoring other engineers. Your role is crucial in ensuring the healthcare search experience runs smoothly and efficiently for our users.

Candidates are expected to have at least 7 years of professional development experience, a firm grasp of machine learning techniques, and experience in building scalable consumer-facing applications. Additionally, a degree in Computer Science or a related field is preferred to provide a solid foundation for the role.

At Zocdoc, we leverage various technologies including Scala, Spark, Databricks, Python, React, C#, and Node.js. We're language agnostic and open to innovative solutions, emphasizing the importance of building robust ML models that scale effectively.

Working on Zocdoc's Search team is a dynamic experience filled with collaboration and innovation. You’ll be part of a team that drives impactful changes, enhancing the way patients interact with healthcare services. The environment is supportive, fostering mentorship and professional growth, making it a wonderful space for passionate engineers.

Zocdoc is committed to fair and equitable compensation practices. Salary ranges for the Staff Machine Learning Engineer are based on market data and consider individual experience, qualifications, and skills. Competitive base salaries, along with variable pay and equity options, ensure that we attract top talent.

It's beneficial to discuss a specific project where you played a key role. Focus on the problem you aimed to solve, the methodologies you employed, and the measurable results achieved. Highlight your contributions, especially any innovative solutions you implemented.

Discuss your approach to model evaluation and validation techniques, such as cross-validation, A/B testing, and monitoring performance in production. Emphasize the importance of continuous learning and optimization to maintain model accuracy.

Talk about your experience providing feedback, conducting code reviews, and facilitating learning opportunities, such as workshops or study sessions. Share specific instances where your guidance had a positive impact on a colleague's growth.

Explain your experience with ML Ops, focusing on how you've implemented CI/CD processes for model deployment, monitoring, and maintenance. Provide examples of tools or frameworks you've utilized to streamline these operations successfully.

Be prepared to discuss specific technical challenges, such as data handling, performance scaling, or deployment issues. Highlight how you approached these challenges, including any innovative solutions you proposed or implemented.

Share examples of projects where you've collaborated with product managers or designers. Discuss the importance of communication and how you ensure alignment on goals and deliverables for successful project outcomes.

Mention the importance of a strong foundation in machine learning principles, alongside effective collaboration skills. Argue how the ability to adapt and apply knowledge to real-world problems significantly impacts overall project success.

Talk about a situation where you faced a decision that involved weighing performance, resource use, and model accuracy. Highlight how you approached the trade-off, detailing the rationale behind your final decision.

Explain your structured approach to testing, including unit tests, integration tests, and validating ML models against pre-defined metrics. Emphasize the importance of comprehensive testing to ensure reliability.

Indicate your commitment to continuous learning. Discuss attending conferences, participating in online courses, reading research papers, or being part of professional networks. Convey your enthusiasm for applying new techniques to enhance your work.