Member of Technical Staff, Model Serving

To become a Member of Technical Staff at Cohere, you should have experience serving machine learning models in production, designing scalable services, and ideally, familiarity with deep learning model inference, particularly Transformer architectures. Strong knowledge of distributed systems and cloud infrastructure, such as AWS or GCP, is also crucial. If you are proficient in Golang or similar high-performance languages and are passionate about AI, we encourage you to apply even if your experience doesn't perfectly match our criteria.

As a Member of Technical Staff at Cohere, your daily responsibilities will involve developing, deploying, and operating our AI platform. You’ll work on delivering optimized large language models through API endpoints while ensuring low latency and high availability. Additionally, you will collaborate with other teams, benchmark system performance, and engage with customers to provide tailored deployments that meet their needs.

At Cohere, you can expect an open and inclusive culture that fosters collaboration and innovation. We prioritize a flexible work environment and support our employees with weekly lunch stipends, comprehensive health benefits, and generous vacation policies. You'll be part of a diverse team of top professionals passionate about AI, working closely together to achieve our common mission.

Cohere is committed to the growth and well-being of its employees. As a Member of Technical Staff, you will benefit from personal enrichment programs that support your acquisition of skills, arts and culture pursuits, fitness, and workspace improvement. We also provide a supportive environment for mental health, alongside generous parental leave policies.

Cohere deeply values diversity and inclusivity and strives to create an environment where all voices are heard. We actively welcome applicants from a range of backgrounds and are committed to equal opportunity. We encourage everyone to apply and seek accommodations whenever needed during the recruitment process, ensuring a supportive experience for all potential candidates.

In answering this question, provide specific projects where you’ve deployed ML models, detailing the types of models you worked with, the challenges you faced, and how you overcame them. Highlight what you learned during the process, such as scaling challenges or optimizing latency.

Discuss the tools and methods you typically use for performance benchmarking, such as profiling tools and metrics you track. Share a specific example of how you’ve applied these strategies to improve model performance and the impact it had on the system.

Explain your approach to maintaining low latency in model serving. This might include talking about architectural decisions, optimization techniques you've implemented, and balancing the trade-offs between performance and resource management.

Your response should outline your understanding of distributed systems, including how they contribute to scalability and reliability in model serving. Share experiences where you've implemented distributed solutions and lessons learned from those implementations.

Discuss your experiences with cloud platforms such as AWS or GCP, including specific services you've utilized for deploying machine learning models. Share insights regarding cost management, scalability, and ease of deployment.

In your answer, provide examples of integration challenges, such as compatibility issues or data format discrepancies. Discuss how you collaborated with other teams to solve these challenges and how you ensured successful integration.

This is the chance to highlight your proficiency in languages like Golang and discuss any relevant projects where such languages were key. Explain the improvements you noticed in performance and reliability of systems as a result of using these languages.

Share a specific instance where you worked closely with a customer to understand their needs and tailor a model deployment. Highlight the steps you took, challenges faced, and the ultimate outcome of this customization.

Discuss the resources you use, such as blogs, forums, or online courses, and any conferences you attend. Share how your continuous learning has positively impacted your projects and enhanced your skills.

Provide a personal touch in your response by discussing what excites you about Cohere's mission and culture. Talk about your passion for AI and how this role aligns with your career goals and interests in building scalable AI solutions.