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Director, ML Engineering – Infrastructure
TubiDirector of ML Engineering leading a hybrid team at Tubi, overseeing scalable ML systems and infrastructure design. Fostering innovation and collaboration in a fast-paced environment.
Tech Stack
Tools & technologiesAWSDistributed SystemsPyTorchTensorflow
About the role
Key responsibilities & impact- Lead and manage high-performing teams across ML engineering and ML infrastructure, fostering a culture of innovation, collaboration, and growth.
- Define and execute the strategic roadmap for ML systems, including recommendation, personalization, and ads optimization.
- Oversee the design, development, and deployment of scalable ML pipelines: data ingestion, feature engineering, model training, evaluation, and serving.
- Architect distributed systems to support ML workloads at scale, ensuring reliability, observability, and operational excellence.
- Partner closely with Product, Engineering, and Content teams to align on business goals and deliver impactful ML-driven experiences.
- Support best practices in experimentation, evaluation, and ML system monitoring.
- Ensure cost efficiency, scalability, and performance in ML infrastructure investments.
Requirements
What you’ll need- 10+ years of industry experience spanning machine learning engineering and distributed systems.
- 3+ years of leadership and management experience, with a proven ability to build and lead strong technical teams.
- MSc or Ph.D. in Computer Science, Machine Learning, or related field, or equivalent practical experience.
- Proven expertise in building and deploying end-to-end ML systems at scale, including recommendation and personalization systems.
- Strong background in distributed systems architecture, including low-latency services, streaming platforms, and large-scale serving.
- Hands-on experience with deep learning frameworks (e.g., TensorFlow, PyTorch) and ML infrastructure technologies.
- Track record of delivering high-quality, scalable, and fault-tolerant systems.
- Excellent communication skills and ability to influence product and technical strategy.
- Proven experience deploying large-scale serving systems on AWS and demonstrated expertise in leveraging Databricks for large-scale data processing and ML workflows
Benefits
Comp & perks- Annual discretionary bonus
- Long-term incentive plan
- Medical/dental/vision insurance
- Vacation/paid time off
- Flexible Time Off Policy
- Generous Parental Leave Program
- Wellness reimbursement