ML Research Engineer (San Jose)

As an ML Research Engineer at Coactive, your main responsibilities will include solving complex machine learning challenges, collaborating with a talented team to develop innovative algorithms, conducting thorough research and experiments, and mentoring fellow team members to foster a collaborative culture.

To qualify for the ML Research Engineer position at Coactive, candidates should have a Bachelor's degree in Computer Science or a related field, or equivalent experience, along with a minimum of 5 years of experience in writing production-grade code and developing pipelines for large-scale ML training.

ML Research Engineers at Coactive should be proficient in Python, Java, or C++ and have experience with cloud-native platforms like AWS, Azure, or GCP. Familiarity with tools such as PyTorch and TensorFlow is also essential for building and maintaining machine learning models.

Candidates can expect a supportive and engaging workplace culture at Coactive, where innovative ideas are nurtured. The company values its employees, offering unlimited PTO, social events, and a focus on personal and professional development to foster a positive work environment.

The estimated salary range for ML Research Engineers at Coactive is between $185,920 and $212,800 per year. This range may vary based on individual qualifications, market demand, and overall business needs.

When discussing your experience, focus on specific algorithms you've implemented, the outcomes of your projects, and how you overcame any challenges you faced during the process.

Talk about the various techniques you utilize, such as hyperparameter tuning, regularization, and leveraging ensemble methods. Providing examples from past projects can enhance your answer.

Explain your systematic approach to debugging, which often includes data validation, visualizing model predictions, and understanding where the model may not meet expectations.

Share your experience in deploying models on platforms like AWS or GCP. Highlight specific projects where you built and maintained ML pipelines to showcase your technical skills.

Discuss a particular project where your contributions significantly impacted its success, detailing your role, challenges, and how you drove the project to completion.

Emphasize your commitment to open communication, sharing knowledge with peers, and leveraging group brainstorming sessions to foster collaboration and creativity within the research team.

Provide examples of how you've mentored others, focusing on specific instances where you helped them navigate technical challenges or develop professionally.

Share your perspective on industry trends, discussing the potential of machine learning to enhance data analysis, automate processes, and drive strategic decision-making in enterprises.

Discuss your strategies for keeping informed, such as following leading publications, engaging in online forums, attending conferences, or collaborating with fellow researchers.

Express what aspects of the role resonate with you, such as working on cutting-edge projects or the opportunity to collaborate with industry experts, to illustrate your genuine enthusiasm.