Senior ML Platform Engineer

As a Senior ML Platform Engineer at Synthesia, your key responsibilities include developing and maintaining our ML platform, managing cloud infrastructure, supporting research teams to scale AI models, and optimizing GPU clusters. You will work closely with researchers to enhance their efficiency and provide a collaboration environment where they can thrive.

To be considered for the Senior ML Platform Engineer role at Synthesia, candidates should have a background in Computer Science, with at least three years of industry experience. Proficiency in Python, knowledge of CI/CD pipelines, experience with Kubernetes, and familiarity with major cloud providers like AWS, GCP, or Azure are essential qualifications.

A Senior ML Platform Engineer at Synthesia should have experience with GPU-based HPC clusters, containerization technologies, and workflow orchestrators such as Kubeflow, Airflow, or Dagster. Familiarity with cloud services is also vital as you will be working in a cloud-first environment.

At Synthesia, we are dedicated to the professional development of our team members. As a Senior ML Platform Engineer, you will find ample opportunities for career growth, participate in company retreats, and engage in regular planning and social events, all designed to nurture skills and foster collaboration.

The work culture at Synthesia is dynamic and inclusive. As a Senior ML Platform Engineer, you will enjoy a hybrid work environment, supportive team collaboration, and a company atmosphere that values open communication, creativity, and employee well-being, while having fun along the way!

When answering this question, detail your specific experiences with cloud platforms like AWS, GCP, or Azure. Highlight projects where you set up ML infrastructures, the challenges faced, and how you overcame them, emphasizing any notable outcomes.

Discuss your methodologies for implementing CI/CD in ML projects, including the tools you have used. Provide examples of how you've automated testing and deployment, which can greatly enhance a team's workflow efficiency.

Be prepared to elaborate on specific GPU technologies and architectures you’ve used. Explain how you optimized workloads and improved model training performance to drive impactful results in your past roles.

Share a specific example that illustrates your proactive approach to enhancing developer experience. Focus on the tools or processes you introduced and the positive feedback received from data scientists as a result.

Talk about your hands-on experience with Kubernetes and container orchestration. Mention projects where you deployed applications in a Kubernetes environment and how that facilitated smoother operations for teams.

Discuss your experience with tools like Kubeflow, Airflow, or Dagster. Elaborate on the use cases for these tools in your previous projects, and how they contributed to managing and scheduling workflows efficiently.

When answering, emphasize your commitment to maintaining technical documentation. Provide examples of how your documentation practices improved team collaboration and reduced onboarding times for new team members.

Describe your strategies for fostering communication between teams, such as regular check-ins, collaborative tools, and feedback loops. Highlight past successes in promoting a culture of openness and teamwork.

Make your answer personal and passionate. Discuss what excites you about Synthesia's mission, the potential for growth in AI technology, and how your skills align with the company's goals.

Give an honest perspective about the landscape of ML engineering, mentioning challenges related to scalability, collaboration, and evolving technology. Discuss how you plan to approach these challenges based on past experiences.