Job Description
The role involves leading machine learning projects, developing scalable ML infrastructure, and collaborating with cross-functional teams to deliver AI-driven products that create measurable customer impact within an AI-first company.
Key Responsibilities
- Lead and guide ML projects across multiple teams, setting technical direction and platform capabilities.
- Collaborate with applied scientists, software engineers, product, and customer success teams to deliver production ML systems.
- Design and build ML platform components such as feature stores, model registries, and serving infrastructure.
- Develop automated training and deployment pipelines to support model improvement and handle data drift.
- Create real-time and batch feature engineering systems for identity resolution, customer segmentation, and predictive modeling.
- Optimize model inference latency to meet service level agreements while managing infrastructure costs.
- Establish MLOps best practices, operational standards, and monitoring to ensure reliable and maintainable ML systems.
- Mentor engineers and influence the technical strategy for ML infrastructure and systems.
Requirements
- 8 years of experience building production ML systems, with experience designing ML infrastructure and platforms.
- Technical leadership experience driving ML platform evolution or major ML projects across multiple teams.
- Expertise in ML deployment patterns, model serving, feature engineering, and monitoring observability for ML systems.
- Software engineering skills with experience in Python and familiarity with ML frameworks e.g. XGBoost, PyTorch, PySpark.
- Experience with cloud-native ML infrastructure, containerization, and orchestration tools such as Kubernetes and Docker.
- Enthusiastic about AI-first development practices, with experience using AI coding assistants like Claude Code to accelerate engineering workflows.
- Turn ambiguous ML infrastructure problems into relevant plans, and guide teams through delivery.
- Experience aligning ML technical strategy with organizational priorities and customer needs.
- Mentor engineers, improving teams, and improving how ML systems are built and operated.
Benefits & Perks
Compensation/salary range: 190,000-260,000 USD base salary
Hybrid work schedule with three days in the office each week
Employee healthcare coverage
Transportation subsidies
Self-managed PTO
Flexible work arrangements
Stock options and equity grants
Work environment with snacks and employee experience perks like events and activities
Inclusive and supportive company culture
Ready to Apply?
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