Design, build, and evolve core ML platform infrastructure, including feature stores, real-time model scoring services, and systems supporting the full model development, deployment, and monitoring lifecycle.
Drive technical decision-making for complex initiatives, choosing solutions that scale, are testable, and reduce long-term maintenance burden.
Lead and influence system design discussions, clearly articulating trade-offs and aligning solutions with product and business goals.
Set a high bar for code quality and system reliability through exemplary contributions and thoughtful, constructive code reviews.
Identify, communicate, and mitigate technical risks across platform components before they impact members.
Partner closely with data scientists, engineers, and product stakeholders to translate modeling and business needs into durable platform capabilities.
Provide clear, reliable estimates for complex projects, including assumptions, risks, and dependencies.
Improve team processes, tooling, and standards to increase engineering quality and delivery velocity.
Mentor and support other engineers through design feedback, code reviews, and onboarding.
Participate in hiring and interviews, helping raise the technical bar through well-calibrated feedback.

Bachelor’s degree in Computer Science or a related field, or equivalent practical experience. Advanced degrees are a plus.
5+ years of professional software engineering experience, with a focus on backend, platform, or infrastructure engineering.
Deep expertise in Python; proficiency in an additional language is a plus.
Strong experience building or operating scalable, high-availability distributed systems in a cloud environment (GCP, AWS).
Experience working with ML systems from an infrastructure perspective, including deployment, serving, monitoring, and data access.
Proficiency with SQL and relational databases; familiarity with Snowflake or non-relational systems is a plus.
Experience leading complex technical projects from design through production.
Experience with MLOps tooling or feature store architectures is a nice to have.
Experience with workflow orchestration tools (e.g., Airflow) and large-scale data processing frameworks (e.g., Spark, Beam) is also a nice to have.
Background building data-intensive or real-time systems is a nice to have.

Premium Medical, Dental, and Vision Insurance plans
Generous paid parental and caregiver leave
401(k) savings plan with matching contributions
Flexible PTO and generous company holidays, including Juneteenth and Winter Break
Flexible hours and virtual-first work culture with a home office stipend
Financial advisor and financial wellness support
Opportunity to tackle tough challenges, learn and grow from fellow top talent, and help millions of people reach their personal financial goals
All-company in-person events once or twice a year and virtual events throughout to connect with your team members and leadership team

Senior Machine Learning Platform Engineer

Key skills