Staff Machine Learning Engineer - Infrastructure
Skills
- Deep expertise in ML architecture and MLOps
- Strong experience with AWS managed services for data, training, deployment, and monitoring
- Experience building feature stores and scalable data pipelines
- Proven background in inference and model monitoring infrastructure
- Ability to deliver compliant architectures in regulated industries (financial services preferred)
- Strong technical communication and stakeholder influence skills
Responsibilities
- Define and drive the technical vision for ML infrastructure to support end-to-end model lifecycle (prototyping, data/feature pipelines, training, deployment, monitoring)
- Design integrations between ML infrastructure and core product/services to deliver highly available ML capabilities
- Build and operate ML infrastructure on AWS using managed services, ensuring operational excellence and SLA adherence
- Collaborate with ML Science, Product, Engineering and Security to establish governance, security, and best-practice standards
- Drive adoption of MLOps practices, mentor Data+ML engineers, and provide career guidance
- Create technical designs, product requirements, and documentation to inform implementation
Education
- BS degree preferred (or equivalent practical experience)
- Advanced degree (MS/PhD) preferred but not required
Benefits
- Flexible First work model (remote or Oakland HQ) and location-calibrated pay tiers
- Annual bonus eligibility
- Multiple health insurance options
- Flexible time off
- Retirement savings program with company contribution
- Equity in a publicly traded company and ESPP
- Family-forming benefits and up to 20 weeks parental leave
- Free therapy sessions, financial/professional coaching, and legal advice
- Monthly remote-work stipend and annual development dollars
Average salary estimate
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