Join Withshepherd.com as their first Machine Learning Engineer to build the ML function from scratch. You will design, train, and deploy production systems that automate complex underwriting workflows. This high-ownership role focuses on advancing the platform toward fully autonomous insurance decisions, leveraging LLMs and agentic frameworks to transform how construction and industrial risks are priced and managed.
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Founding Machine Learning Engineer at Withshepherd
Withshepherd.com is hiring their first Machine Learning Engineer to build an autonomous underwriting engine from the ground up. As the founding ML hire in San Francisco, you’ll own the entire lifecycle—from fine-tuning LLMs to designing production architecture. If you have 4+ years of experience shipping production models and want to lead a technical function at a company redefining risk for the physical world, this is your 0-to-1 opportunity.
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Location
San Francisco, United States
Compensation
$180k-$220k + Equity
Company
Shepherd
Role overview
AI-native commercial insurance
Shepherd is an AI-native commercial insurance platform transforming how high-hazard industries get covered. Our mission is to make risk frictionless for the builders and operators shaping the physical world — protecting progress from concept through construction and into decades of operation.
What you will do
- Own the end-to-end ML lifecycle, from designing raw data pipelines and training fine-tuned models to deploying and monitoring production systems on AWS Sagemaker.
- Build and close feedback loops that convert human underwriter domain expertise into training signals, enabling continuous model improvement and autonomous capability progression.
- Develop advanced evaluation frameworks and confidence scoring systems to safely scale LLM-driven agentic workflows across the entire commercial underwriting lifecycle.
Who this is a fit for
- Has 4+ years of industry experience shipping production ML systems end-to-end, with deep proficiency in Python, PyTorch, and modern model deployment platforms.
- Possesses hands-on experience fine-tuning LLMs using techniques like RLHF or DPO and building reliable agentic workflows with structured outputs and tool use.
- Thrives in high-ambiguity, 0-to-1 environments and is committed to working on-site in San Francisco to collaborate directly with underwriters and engineers.
Why this role is remarkable
- Build an ML function from zero at a high-growth startup, defining the architecture, tooling, and evaluation frameworks that will shape the company's technical foundation.
- Work at the frontier of agentic AI, moving beyond simple chatbots to building fully autonomous systems that handle high-stakes financial decisions in the physical world.
- Join a well-capitalized Series B team with $60M+ in total funding from top-tier investors like Spark Capital, Costanoa Ventures, and Intact Private Capital.
How Jack & Jill work together
I get to know what you’re great at, then find roles you’d never find yourself.Ok, I'll go first. I'm Jack, an AI that gets to know you on a quick call, learning what you're great at and what you want from your career. Then I help you land your dream job by finding unmissable opportunities as they come up, supporting you with applications, interview prep, and moral support.
I recruit from Jack’s network and make the intro when I spot a great match.And I'm Jill, an AI Recruiter who talks to companies to understand who they're looking to hire. Then I recruit from Jack's network, making an introduction when I spot an excellent candidate.
Jack gets to know what you're great at and what you want next, then searches 15 million jobs daily and helps you discover roles at companies like this.
Meet Jack
What happens next?
Jack’s an AI agent for job searching and career coaching. He works for you.
Jill is the AI recruiter working for the company. She recruits from Jack’s network.
If your profile’s a match and Shepherd wants to meet, Jill will make the intro. In the meantime, Jack will send you excellent alternatives.