Forward Deployed Engineer (Training)

present of AI; San Francisco (CA); On-site; USD 150,000 - 230,000; Full time

Job summary

Baseten is seeking a hands-on AI engineer to work closely with customers, owning the technical outcome for a few large AI accounts. You will transform vague goals into proof-of-concept and scale to production, debugging from logs and metrics and communicating clearly with both engineers and leaders.

Focus areas include running and improving AI models, tuning inference, building evals, and producing impact at scale. Baseten emphasizes production model serving and collaboration across teams.

Qualifications

• Experience shipping in production systems.
• Ability to debug from logs and metrics.
• Clear customer-facing technical communication.

Responsibilities

• Own the technical outcome for major AI accounts.
• Tune inference, build evals and improve models.
• Explain complex topics clearly to engineers and leaders.

Skills

Production debugging

Kubernetes

Ray

Model serving

Tools

PyTorch

Spark

Job description

This is a hands-on engineering role that sits close to customers. The person owns the technical outcome for a few large AI accounts, from shaping a vague goal into a working proof of concept and then a production setup. A strong candidate has shipped code in real production systems, can debug unfamiliar problems from logs and metrics, stays calm when things break, and explains complex topics clearly to both engineers and leaders.

How it fits the AI era

The work centers on running and improving AI models for other AI companies, so AI is the subject of the job as well as a tool. Tuning inference, building evals, and improving models through post-training are all part of the day. What stays human is judgment under pressure, deciding what to fix first, earning customer trust, and turning what accounts need into product direction.

How it builds AI experience

Someone in this role would get close exposure to how leading AI products are served and improved at scale, across inference and post-training. To build toward it, try running open source models with an inference engine, measure their speed and quality, and practice fine tuning a small model with a clear eval. Baseten's focus on production model serving makes those skills directly relevant.

Worth showing

• Production debugging with logs and traces
• Kubernetes or Ray for GPU workloads
• Building evals and benchmarks
• Clear customer-facing technical communication

This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.

About Baseten

High-performance cloud inference platform for deploying, serving, and scaling open-source and custom artificial intelligence models.

Tools mentioned: forward deployed, Spark, Kubernetes, PyTorch, machine learning, deep learning