About this role
Building and operating the infrastructure that powers machine learning systems at scale constitutes the primary focus of this role. Joining the Predictive and Generative AI team means taking ownership of the underlying platform to make deploying, operating, and scaling models fast, safe, and efficient. Complex infrastructure initiatives are carried out from design through production, including multi-region model serving fleets and the pipelines keeping hundreds of customer-specific models healthy.
Driving transformative initiatives that change how machine learning runs in production requires deep expertise in distributed systems. Technical vision and production quality standards are established by this engineer, who sets direction for model training, deployment, serving, and observation. Collaboration with partner teams across shared infrastructure and data systems ensures engineering quality is raised through rigorous design reviews and mentorship.
What you will do
- Identify and drive transformative initiatives that change how the team runs machine learning in production.
- Build and ship at high velocity by carrying complex infrastructure initiatives from design through production.
- Own the platform technical vision, set production quality bars, and lead incident response for machine learning systems.
- Drive reliability and cost work to keep the platform efficient at scale while managing cross-team technical relationships.
- Raise engineering quality through design reviews, code reviews, and production readiness evaluations while mentoring senior engineers and data scientists.
- Connect technical decisions to customer and business outcomes while representing the technical perspective to leadership.
What you need
- Bring eight or more years of experience building and operating distributed systems in production with a focus on deployment and operations.
- Demonstrate hands-on experience with production machine learning workloads such as training pipelines, model serving, feature systems, or platform tooling.
- Possess deep working knowledge of Kubernetes and cloud infrastructure, including identity and access management, networking, and cost profiles.
- Show a history of technical leadership by owning direction for a team, leading multi-quarter cross-functional initiatives, and growing senior engineers.
- Demonstrate effective verbal and written communication skills to build consensus and drive decision-making.
Nice to have
- Queueing and orchestration systems such as Celery, RabbitMQ, Kafka, or Ray
- ML platform tooling such as MLflow, feature stores, or ML observability
- Experience in Python, Ruby on Rails, MongoDB, Redis, and Kubernetes
- Operating under compliance regimes like SOX or HIPAA
- Customer engagement, personalization, or marketing technology domain experience
Benefits
- Competitive compensation that may include equity
- Retirement and Employee Stock Purchase Plans
- Flexible paid time off
- Comprehensive benefit plans covering medical, dental, vision, life, and disability
- Family services including fertility benefits and equal paid parental leave
- Professional development supported by formal career pathing, learning platforms, and a yearly learning stipend
Skills and tech stack
Questions candidates ask
What is the salary range for this position?
For candidates based in the United States, the pay range at the start of employment is expected to be between $184,000 and $314,000 per year. The expected On Target Earnings range is between $204,000 and $348,000 per year, including bonus or commission.
What kind of benefits are included?
The role qualifies for a comprehensive Total Rewards package that includes equity grants of restricted stock. Additional benefits include retirement plans, flexible paid time off, medical and dental coverage, parental leave, and professional development support.
What is the team and mission for this role?
The position sits on the Predictive and Generative AI team, which delivers engaging and personalized customer experiences through machine learning and AI-enhanced marketing solutions. The team runs these solutions as production systems at global scale.
What technical background is required?
Candidates need eight or more years building and operating distributed systems in production, with depth in deployment and operations. Hands-on experience with production machine learning workloads and working knowledge of Kubernetes and cloud infrastructure are also necessary.