Jobs at Baseten

Own and improve Baseten’s billing and revenue infrastructure, including pricing, invoicing, metering, reporting, and Orb integrations. Build internal tooling for quoting, approvals, renewals, usage reconciliation, and revenue reporting. Partner with Finance, Sales, GTM, and engineering teams while driving reliability, monitoring, incident response, production debugging, and scalable technical solutions.

Lead the design, implementation, rollout, and operation of Baseten’s identity and authorization platform. Build fine-grained permissions, credential systems, enterprise identity lifecycle capabilities, and administrative governance tools for multi-tenant systems. Partner with product, design, and customer-facing teams to translate enterprise security needs into scalable solutions. Establish practices for security, quality, observability, and operational ownership while mentoring future team members.

Build and operate Baseten’s partner platform by leading integrations with model labs, hyperscalers, developer platforms, and infrastructure companies. Design APIs, service boundaries, provisioning, identity, inference, metering, billing, observability, and reusable integration primitives. Collaborate directly with partner engineering, Product, and Partnerships teams while writing substantial production code, managing cross-team delivery, and ensuring reliability through testing, safe rollouts, failure handling, and measurable service-level objectives.

Owns the operating system for Baseten’s Forward Deployed Engineering team. Responsibilities include managing priority-account cadences, cross-team infrastructure programs, escalations, dependencies, metrics, engineering toil reduction, onboarding, capacity planning, and customer launches. The role coordinates technical teams and leadership across ML infrastructure, inference, platform engineering, Sales, and Solutions Architecture while driving accountability, risk mitigation, and clear executive communication.

Manage and mature Baseten’s security governance, risk, compliance, privacy, audit, certification, vendor assessment, and customer assurance programs. Translate regulatory and customer requirements into technical controls and product requirements, partnering with Engineering, Product, Security, Legal, and Operations. Coordinate SOC 2, ISO, HIPAA, FedRAMP, and related audits; manage risk assessments, evidence collection, remediation, security questionnaires, training, and continuous improvement in a fast-growing AI platform company.

Develop and productionize high-performance LLM inference techniques across runtimes, scheduling, serving, routing, and GPU kernels. Profile and optimize latency, throughput, memory usage, and cost; support new models and hardware; build benchmarking frameworks; and contribute to open-source inference engines such as vLLM, SGLang, and TensorRT-LLM. The role requires strong programming, GPU architecture, ML library, and LLM optimization knowledge.

Build and operate the distributed inference platform powering large-scale LLM deployments. Responsibilities include orchestration, routing, autoscaling, scheduling, Model APIs, authentication, quotas, metering, observability, benchmarking, and production reliability across Kubernetes, networking, distributed runtimes, and GPU workloads. The role owns projects end to end and partners with performance engineers to deliver efficient, scalable AI inference capabilities and strong developer experiences.

As a Forward Deployed Engineer, you will develop and maintain ML solutions, collaborate with teams on AI/ML pipelines, and manage projects end-to-end.

Design and implement RDMA/InfiniBand networking primitives for distributed GPU inference, optimize communication for Disaggregated KV Cache and WideEP, enable sub-10s model startup, validate hardware performance on H100/H200/B200/B300/GB300 clusters, build observability tools, and optimize or author communication kernels (NCCL/NVSHMEM/UCX).

As a Software Engineer, you'll build and maintain Baseten's core product, design APIs, implement features, fix issues, and collaborate with teams.

Develop and maintain components for machine learning inference platform. Focus on Kubernetes deployments, resource management, and performance monitoring.

Build and maintain responsive, accessible frontend interfaces using React and TypeScript. Collaborate with product, design, and backend teams to launch performant, scalable UIs, create reusable component libraries, define APIs, mentor engineers, and drive frontend best practices.

Build an internal AI developer platform that provisions agent configs, context tooling, eval harnesses, and rollout mechanics to make AI-assisted engineering the default. Integrate LLMs and third-party coding tools, enable self-serve agent creation, embed safety and audit controls, and measure impact on developer productivity.

The GPU Kernel Engineer will design and implement high-performance GPU kernels, optimize code, and collaborate with research teams to enhance AI workloads.

Lead development of enterprise platform features focusing on infrastructure, security, and compliance for enterprise customers. Oversee projects, collaborate cross-functionally, and mentor engineering teams.

As an AI Support Engineer, you'll manage support requests, resolve user issues, optimize ML models, and contribute to product development.

As a Site Reliability Engineer, you'll build and maintain infrastructure for ML models, automate processes, and collaborate cross-functionally.

Build and evolve scalable observability systems: high-throughput telemetry ingest, cost-efficient storage, instrumentation libraries, SLO/alerting infrastructure, and integrations to improve reliability, diagnostics, and incident response across multi-cloud infrastructure.

The Software Engineer will lead the development of Baseten Voice AI, focusing on real-time model serving systems, collaboratng with teams on architecture and implementation.

Partner with customers to architect, build, and deploy high-scale production AI applications. Translate ambiguous goals into reliable, observable services, implement end-to-end model deployment and monitoring, optimize ML projects, and own customer-facing technical delivery and product outcomes.