Site Reliability Engineer (SRE)

ABOUT BASETEN

Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $300M Series E, backed by investors including BOND, IVP, Spark Capital, Greylock, and Conviction. Join us and help build the platform engineers turn to to ship AI products.

THE ROLE

As a Site Reliability Engineer, you'll envision and build robust systems and processes that ensure our infrastructure is scalable, reliable, and efficient. This can range from automating deployments and monitoring systems to optimizing performance and managing incidents.

We all work closely with our users, learning from their past struggles in operationalizing ML, onboarding them onto our platform, and turning our learnings into ideas for improving Baseten.

EXAMPLE INITIATIVES

You'll get to work on these types of projects as part of our Infrastructure team:

• Multi-cloud capacity management

• Inference on B200 GPUs

• Multi-node inference

• Fractional H100 GPUs for efficient model serving

RESPONSIBILITIES

• Build and maintain scalable infrastructure to support the deployment and operation of machine learning models.

• Establish standards and best practices for reliability and performance across the infrastructure.

• Automate processes when relevant, particularly for managing CI/CD pipelines.

• Own products and projects end-to-end, functioning as both an engineer and a project manager, with a focus on user empathy, project specification, and end-to-end execution.

• Collaborate with cross-functional teams to understand project requirements and translate them into technical solutions.

• Mentor junior team members and contribute to knowledge sharing within the organization.

• Navigate ambiguity and exercise good judgment on tradeoffs and tools needed to solve problems, avoiding unnecessary complexity.

• Demonstrate pride, ownership, and accountability for your work, expecting the same from your teammates.

REQUIREMENTS

• Bachelor's, Master's, or Ph.D. degree in Computer Science, Engineering, Mathematics, or related field.

• Extensive experience with Kubernetes.

• Experience in building and maintaining scalable infrastructure.

• Experience with infrastructure-as-code tools (e.g., Terraform, CloudFormation, Pulumi) and CI/CD tooling (e.g., GitHub Actions, GitLab CI, Circle CI, Jenkins).

• Relevant OSS observability experience (Prometheus, ELK stack, Grafana stack, Opentelemetry) is a plus.

• Ability to own projects end-to-end, from project specification to execution.

• No prior machine learning experience required, but should be open to learning about it.

BENEFITS

• Competitive compensation, including meaningful equity.

• 100% coverage of medical, dental, and vision insurance for employee and dependents

• Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!)

• Paid parental leave

• Fertility and family-building stipend through Carrot

• Company-facilitated 401(k)

• Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities.

Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you.

At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status.

We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).