Forward Deployed Engineer

Autoscience•Menlo Park, CA

15d•Onsite

About The Position

We’re looking for a full-time on site role for a Forward Deployed Engineer to bring AutoScience into the most important machine learning environments in the world. You’ll work directly with customers to understand their ML systems, deploy AutoScience into their infrastructure, and turn ambiguous modeling problems into environments where our automated researchers can run thousands of useful experiments. This is not a traditional solutions engineering role. You’ll write production code, work inside real ML pipelines, debug distributed systems, reason about machine learning challenges, and collaborate directly with our research and engineering teams to make the core product better. You’ll own deployments end-to-end from the first technical conversation to the first model improvement shipped into production.

Requirements

Unusually strong engineering skills with a preference for operating where the problem is poorly specified.
Strong interpersonal judgment and communication skills.
Ability to build trust with teammates and stakeholders.
Ability to collaborate effectively across different working styles and areas of expertise.
Comfort opening an unfamiliar ML repository, understanding how the system works, getting it running, identifying what matters, and shipping something useful quickly.
Strong software engineering fundamentals and excellent Python skills.
Experience building, training, evaluating, or deploying machine learning systems.
Experience working with production cloud infrastructure and containerized workloads.
Strong debugging skills across software, infrastructure, data, and ML systems.
Experience owning technically complex projects from an ambiguous starting point through production.
Good judgment about when to build something robust and when to move quickly.
Ability to communicate deeply technical ideas with researchers, engineers, and technical leaders.
Eagerness to spend significant time working directly with customers.
High degree of autonomy and comfort working in a fast-moving environment.

Nice To Haves

Enjoyment from socializing with technical and non-technical customers.

Responsibilities

Work directly with customers to understand their ML systems.
Deploy AutoScience into customer infrastructure.
Turn ambiguous modeling problems into environments where automated researchers can run thousands of useful experiments.
Write production code.
Work inside real ML pipelines.
Debug distributed systems.
Reason about machine learning challenges.
Collaborate directly with research and engineering teams to make the core product better.
Own deployments end-to-end from the first technical conversation to the first model improvement shipped into production.