Staff Tech Lead Manager, ML Data Services

What You'll Do

Technical Leadership and Strategy

• Define and drive the technical roadmap and long-term strategy for the ML Data Service platform, ensuring it meets the evolving needs of ML engineers across the company.
• Serve as the top technical expert for the team, architecting complex, highly scalable, and fault-tolerant systems for data ingestion, transformation, and serving.
• Lead the technical vision for the provision of diverse training data sources, including ingestion from real-world drive logs, resimulation outputs, autolabelling pipelines, and human labelling efforts.
• Drive the implementation of robust mechanisms for monitoring data quality, tracking data lineage, and ensuring overall data governance and compliance.
• Provide hands-on technical guidance, design reviews, and mentorship to engineers, ensuring the adoption of best practices, high code quality, and operational excellence.

Management and Team Growth

• Manage a team of software engineers, including performance reviews, career development, coaching, and mentoring.
• Act as a technical leader-manager hybrid, balancing 50% management duties with 50% technical leadership, architecture, and hands-on coding/design.
• Drive hiring and recruitment efforts to attract, onboard, and retain top engineering talent, fostering a diverse and inclusive team culture.
• Champion engineering excellence, operational rigor, and a strong sense of ownership within the team.

Collaboration and Execution

• Collaborate closely with ML model teams and Infrastructure teams to understand data needs and deliver solutions that accelerate ML development cycles.
• Lead the execution of major projects, ensuring on-time delivery and alignment with business objectives.
• Communicate complex technical concepts and strategic direction effectively to both technical and non-technical stakeholders, including executive leadership.

What We're Looking For

• Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
• 8+ years of experience in software engineering, with a strong focus on large-scale data systems, distributed systems, or ML infrastructure.
• 3+ years of experience managing engineering projects with a track record of delivering efficient tools and infra. Manager experience of building and scaling high-performing teams is a strong plus (including mentoring, performance management, and career development).
• Deep expertise in data processing technologies (e.g., Spark, Flink, Beam, distributed databases) and experience with cloud platforms (e.g., AWS, GCP, Azure).
• Demonstrated experience designing, implementing, and operating production-grade ML data pipelines or feature stores.