Senior Machine Learning Engineer

The ML Platform team is looking for a Senior Machine Learning Engineer to join our team. We are looking for someone with extensive experience in building and operating large-scale ML infrastructure and platforms.

What you’ll do

• Drive the future of our machine learning platform by designing and implementing robust, scalable, and efficient ML infrastructure and tools that empower our product and internal teams to build and deploy ML models faster and with higher quality.
• Collaborate with cross-functional teams (e.g., ML Platform, MLOps, ML Infra, Data Platform, Product Teams) to understand their needs, gather requirements, and deliver innovative solutions that address their challenges and pain points.
• Lead the design and development of critical components of our ML platform, including data pipelines, model training frameworks, model serving infrastructure, and MLOps tools, ensuring high performance, reliability, and maintainability.
• Contribute to the overall architecture and strategy of our machine learning platform, identifying opportunities for improvement, advocating for best practices, and driving the adoption of new technologies and approaches.
• Mentor and guide junior engineers, providing technical leadership, code reviews, and constructive feedback to foster a culture of continuous learning and growth.
• Stay up-to-date with the latest advancements in machine learning, distributed systems, and cloud technologies, evaluating and recommending new tools and techniques that can enhance our platform capabilities.

Who you are

• You have 5+ years of experience as a Machine Learning Engineer or in a related role, with a strong focus on building and deploying ML systems in production environments.
• You have a deep understanding of machine learning principles, algorithms, and best practices, as well as experience with various ML frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
• You have extensive experience with distributed systems and cloud platforms (e.g., AWS, GCP, Azure), particularly in the context of building and operating large-scale ML infrastructure.
• You are proficient in programming languages commonly used in ML (e.g., Python, Scala, Java) and have a solid grasp of software engineering principles, including data structures, algorithms, and design patterns.
• You have experience with MLOps tools and practices (e.g., MLflow, Kubeflow, Airflow, Docker, Kubernetes) for managing the end-to-end ML lifecycle.
• You have excellent communication, collaboration, and problem-solving skills, with a track record of working effectively in cross-functional teams and driving technical projects to successful completion.
• You are passionate about machine learning and its applications, with a desire to contribute to the open-source community and share your knowledge with others.