Forward Deployed Engineer

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 $1.5B Series F, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE As a Forward Deployed Engineer at Baseten, you will partner directly with customers to architect, build, and deploy high-scale production AI applications on Baseten’s platform. You’ll own the journey with customers from initial exploration to production deployment, translating ambiguous business goals into reliable, observable services with clear quality, latency, and cost outcomes. This role is a great fit for entrepreneurial engineers who want a front-row view into how modern companies adopt AI at scale and who enjoy working across product, software development, performance engineering, and customer-facing implementations. To be clear, this is an engineering role with hands-on coding and software development that also includes aspects of product management, technical customer success, and pre-sales solution engineerin [... source excerpt omitted ...] og posts written by members of our Forward Deployed Engineering team: Forward Deployed Engineering on the frontier of AI The fastest, most accurate Whisper transcription Deploy production-ready model servers from Docker images Deploy custom ComfyUI workflows as APIs RESPONSIBILITIES Develop and maintain software systems and product features using one or more general-purpose programming languages in a production-level environment, with a preference for Python due to its relevance in ML projects. Drive customer impact by designing, implementing, and deploying Baseten solutions end-to-end (problem framing → evaluation → production deployment → monitoring). This involves working [... source excerpt omitted ...] mplementation, and expansion. Deliver with velocity: turn vague objectives into clear specs and well-defined PoCs so we can rapidly ship well-tested services and outcomes for our customers Optimize and enhance AI/ML projects, contributing to the continuous improvement of our technical stack. This includes developing features and PRDs with other engineering and product orgs. Own products and customer projects end-to-end, functioning as both an engineer, project manager, and product manager, with a focus on user empathy, project specification, and end-to-end execution. Navigate ambiguity and exercise good judgment on tradeoffs and tools needed to solve problems, avoiding unnece