Job description

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 baseten.co, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE Forward Deployed Engineers work directly with the largest and fastest-growing AI companies in the world, owning their technical outcomes on Baseten and taking on the hardest problems in serving and improving models at scale. The work spans the model lifecycle: inference, post-training, and the systems that tighten the loop between them. - Act as each account's de facto CTO on Baseten, with final accountability for how their workloads are designed, run, and scaled. - Take customer objectives from vague to shipped: frame the problem, define the spec and success criteria, build the PoC, and carry it through to production quickly, using the right tools for the problem. - Design the evals and benchmarks that isolate where quality or performance falls short, then close the gap yourself, whether that means optimizing inference, improving the model through post-training, or reworking the eval itself. - Be the first responder to mission-critical failures including triage, owning the fix directly or route to the owning team and stay accountable until it ships. - Build internal systems so that each engagement is faster than the last. This includes tooling and automation for eval and deployment infrastructure, and the recipes and reference implementations that make the product more self-serve. - Shape the product itself, channeling what your accounts need into the roadmap and shipping fixes and features into Baseten's codebase yourself. - Do all of this across multiple accounts at once, sequencing the work, pulling in the right people at the right time, and keeping customers and internal stakeholders aligned on status and risk. REQUIREMENTS - Minimum 1-2 years of software engineering experience, shipping and maintaining code in large production systems, ideally with breadth across the stack - Experience debugging complex production issues - working through logs, metrics, and traces to root-cause problems in unfamiliar systems - Confidence owning ambiguous technical problems. This includes triaging, making decisions under uncertainty, and knowing when to pull in other engineers who own the underlying systems - Motivation beyond pure engineering. An interest in wanting to work directly with customers, understand their problems firsthand, and influence the product - Clear communication on complex technical topics, whether you're talking to a customer's engineers or their leadership - Genuine curiosity about AI inference and training, and a drive to become an expert in the infrastructure powering it - Willingness to respond to customers outside regular working hours and participate in an on-call rotation - Excitement about solving problems for some of the largest and fastest-growing companies in the world running mission-critical AI workloads WHAT YOU'LL BRING We don't expect any one person to cover all of this - the strongest candidates may spike in one or two of the following: - Depth in a core infrastructure domain such as storage systems or networking (anywhere from the cloud layer to cluster interconnects like InfiniBand and RoCE) - Experience operating distributed compute platforms like Kubernetes, Slurm, or Ray, especially for GPU workloads - A detailed understanding of LLM architectures and modern inference engines like vLLM, TensorRT-LLM, or SGLang - The ability to profile and optimize GPU workloads, in training or serving - Hands-on experience with post-training techniques like SFT and RL, or a broader deep learning background plus fluency in a tensor computation library like PyTorch or JAX - Operational depth - running on-call, leading incident response, debugging distributed systems under pressure BENEFITS - Competitive compensation, including meaningful equity - (U.S. only) 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 - (U.S. only) 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).

Requirements

Must-haves

Act as each account's de facto CTO on Baseten, with final accountability for how their workloads are designed, run, and scaled.
Take customer objectives from vague to shipped: frame the problem, define the spec and success criteria, build the PoC, and carry it through to production quickly, using the right tools for the problem.
Be the first responder to mission-critical failures including triage, owning the fix directly or route to the owning team and stay accountable until it ships.
Shape the product itself, channeling what your accounts need into the roadmap and shipping fixes and features into Baseten's codebase yourself.
Do all of this across multiple accounts at once, sequencing the work, pulling in the right people at the right time, and keeping customers and internal stakeholders aligned on status and risk.
Minimum 1-2 years of software engineering experience, shipping and maintaining code in large production systems, ideally with breadth across the stack
Experience debugging complex production issues - working through logs, metrics, and traces to root-cause problems in unfamiliar systems
Confidence owning ambiguous technical problems. This includes triaging, making decisions under uncertainty, and knowing when to pull in other engineers who own the underlying systems
Motivation beyond pure engineering. An interest in wanting to work directly with customers, understand their problems firsthand, and influence the product
Clear communication on complex technical topics, whether you're talking to a customer's engineers or their leadership
Genuine curiosity about AI inference and training, and a drive to become an expert in the infrastructure powering it
Willingness to respond to customers outside regular working hours and participate in an on-call rotation
Excitement about solving problems for some of the largest and fastest-growing companies in the world running mission-critical AI workloads
We don't expect any one person to cover all of this - the strongest candidates may spike in one or two of the following:
Depth in a core infrastructure domain such as storage systems or networking (anywhere from the cloud layer to cluster interconnects like InfiniBand and RoCE)
Experience operating distributed compute platforms like Kubernetes, Slurm, or Ray, especially for GPU workloads
A detailed understanding of LLM architectures and modern inference engines like vLLM, TensorRT-LLM, or SGLang
The ability to profile and optimize GPU workloads, in training or serving
Hands-on experience with post-training techniques like SFT and RL, or a broader deep learning background plus fluency in a tensor computation library like PyTorch or JAX
Operational depth - running on-call, leading incident response, debugging distributed systems under pressure

Nice-to-haves

None flagged.

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Keywords to mirror

aikubernetesllmmlmachine learning

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Pto

Parental Leave