Founding Machine Learning Engineer
shepherd
7 months ago
Full-time
On-site
San Francisco
$180,000 - $400,000 USD yearly
AI
Moneyboard classification: Published range reaches threshold
Reported USD annual base: $180,000–$400,000
Original source: Ashby
WHAT WE DO
Build the insurance behind the physical world.
The world is building data centers, energy projects, hospitals, schools, and critical infrastructure. Every one of those projects needs insurance to move forward. Yet the process of getting that insurance still depends on scattered documents, manual work, and weeks of back-and-forth.
Shepherd is rebuilding commercial insurance with software, data, AI, and deep underwriting expertise. We started in construction and energy, where understanding the specifics of a project matters and better decisions have consequences far beyond a screen.
We develop insurance products, decide which risks to insure and how to price them, and partner with established insurers to back the coverage. We own the platform our team uses to turn complex submissions into coverage for the builders and operators shaping the world around us.
Real problems. Real ownership.
Our engineers, designers, underwriters, CX, and actuaries build together. The problems span turning messy documents into reliable data, making pricing more precise, and designing AI agents that can take on increasingly complex underwriting work.
The ambition is a platform where experts spend more time on judgment and relationships, supported by software that handles the work around them. Join us to build systems whose decisions help real projects get built.
630+ customers / $500B+ insured project value
[https://app.ashbyhq.com/api/images/user-content/cfdfd0b4-accb-41e6-8804-1216b89e5f82/0348cf6f-15dd-401e-8c20-de06e7a553fc/full-team.jpg]
OUR INVESTORS
Capital to build. Partners to grow with.
$67M total funding / $42M Series B in March 2026
Shepherd is backed by Intact Private Capital, Costanoa Ventures, Spark Capital, Susa Ventures, and Y Combinator. In March 2026, we announced a $42M Series B led by Intact Private Capital, bringing our total funding to $67M.
Intact is also a capacity partner. Alongside its investment, Intact works with Shepherd to bring insurance products to market. That relationship connects our platform and underwriting expertise with the resources of a global insurer.
A bigger opportunity ahead
We are investing in new insurance products, our underwriting team, and the path toward autonomous underwriting. For the people joining Shepherd, that means the chance to shape both the technology and the business it makes possible.
Our backing brings together investors who build technology companies and partners who understand insurance. We are building a company that earns the confidence of both.
[https://app.ashbyhq.com/api/images/user-content/cfdfd0b4-accb-41e6-8804-1216b89e5f82/1441bf79-1d14-43aa-be40-ef718b7fdac3/nyc-wide.jpg]
THE MISSION: FULLY AUTONOMOUS UNDERWRITING
We think about underwriting autonomy the same way Waymo thinks about self-driving cars. Not as a binary switch, but as a graduated progression through defined capability levels. Today, Shepherd sits at the border of L1 for our first Operational Design Domain. You will build the ML systems that carry us from L1 to L3 and beyond. Every model you ship, every feedback loop you close, and every confidence threshold you calibrate is one more autonomous mile driven.
THE ROLE
You will be Shepherd’s first Machine Learning Engineer, embedded in the Fully Autonomous Underwriting (FAU) team. This is a high-ownership, high-ambiguity role. There is no existing ML platform to inherit, no established model registry to maintain. You will build those things. You have the opportunity to define the ML function from the ground up at a company building something genuinely new in a large, underserved market
You will work directly with underwriters to deeply understand the domain, and translate that understanding into ML systems that get meaningfully better over time. You will own the full ML lifecycle – from data through to production – and be the connective tissue between the domain expertise that exists in the business and the systems we’re building to scale it.
WHAT YOU’LL DO
This is an end-to-end ML role. You will own the full lifecycle from raw data through to production systems, and work closely with underwriters, engineers, and product to advance FAU through its autonomy levels.
- Design, build, and ship ML systems that power autonomous underwriting decisions in production
- Build and close the feedback loops that turn human underwriter behavior into training signal and compounding model improvement
- Develop confidence scoring and evaluation frameworks that define when the system is ready to take on more autonomy and when to step back
- Work with large language models to build reliable, auditable, and improvable agentic workflows across the underwriting lifecycle
- Partner directly with underwriters to extract domain knowledge, validate outputs, and earn the trust required to expand the system’s operating domain
- Contribute to the observability, monitoring, and guardrail infrastructure that keeps AI underwriting safe as autonomy scales
WHO YOU ARE
Required
- 4+ years of industry experience building and shipping ML systems end-to-end, from raw data to production models, including experience with model deployment platforms (e.g., AWS Sagemaker)
- Experience finetuning SLMs/LLMs, with a preference for experience using techniques like RLHF, DPO, or LoRA.
- Deep proficiency in Python and modern ML frameworks (PyTorch, HuggingFace, Tensorflow, OpenAI Gym/Gymnasium or similar)
- Experience with LLMs in production: prompt engineering, structured outputs, tool use, evaluation, and cost/latency tradeoffs
- Experience building reliable models with limited labeled data, including synthetic data generation, data augmentation, or similar techniques"
- Strong evaluation instincts: you know how to define what ‘better’ means before you build, not after
- Comfort with ambiguity, highly autonomous, and a bias toward building something real over architecting som
Reported USD annual base: $180,000–$400,000
Original source: Ashby
WHAT WE DO
Build the insurance behind the physical world.
The world is building data centers, energy projects, hospitals, schools, and critical infrastructure. Every one of those projects needs insurance to move forward. Yet the process of getting that insurance still depends on scattered documents, manual work, and weeks of back-and-forth.
Shepherd is rebuilding commercial insurance with software, data, AI, and deep underwriting expertise. We started in construction and energy, where understanding the specifics of a project matters and better decisions have consequences far beyond a screen.
We develop insurance products, decide which risks to insure and how to price them, and partner with established insurers to back the coverage. We own the platform our team uses to turn complex submissions into coverage for the builders and operators shaping the world around us.
Real problems. Real ownership.
Our engineers, designers, underwriters, CX, and actuaries build together. The problems span turning messy documents into reliable data, making pricing more precise, and designing AI agents that can take on increasingly complex underwriting work.
The ambition is a platform where experts spend more time on judgment and relationships, supported by software that handles the work around them. Join us to build systems whose decisions help real projects get built.
630+ customers / $500B+ insured project value
[https://app.ashbyhq.com/api/images/user-content/cfdfd0b4-accb-41e6-8804-1216b89e5f82/0348cf6f-15dd-401e-8c20-de06e7a553fc/full-team.jpg]
OUR INVESTORS
Capital to build. Partners to grow with.
$67M total funding / $42M Series B in March 2026
Shepherd is backed by Intact Private Capital, Costanoa Ventures, Spark Capital, Susa Ventures, and Y Combinator. In March 2026, we announced a $42M Series B led by Intact Private Capital, bringing our total funding to $67M.
Intact is also a capacity partner. Alongside its investment, Intact works with Shepherd to bring insurance products to market. That relationship connects our platform and underwriting expertise with the resources of a global insurer.
A bigger opportunity ahead
We are investing in new insurance products, our underwriting team, and the path toward autonomous underwriting. For the people joining Shepherd, that means the chance to shape both the technology and the business it makes possible.
Our backing brings together investors who build technology companies and partners who understand insurance. We are building a company that earns the confidence of both.
[https://app.ashbyhq.com/api/images/user-content/cfdfd0b4-accb-41e6-8804-1216b89e5f82/1441bf79-1d14-43aa-be40-ef718b7fdac3/nyc-wide.jpg]
THE MISSION: FULLY AUTONOMOUS UNDERWRITING
We think about underwriting autonomy the same way Waymo thinks about self-driving cars. Not as a binary switch, but as a graduated progression through defined capability levels. Today, Shepherd sits at the border of L1 for our first Operational Design Domain. You will build the ML systems that carry us from L1 to L3 and beyond. Every model you ship, every feedback loop you close, and every confidence threshold you calibrate is one more autonomous mile driven.
THE ROLE
You will be Shepherd’s first Machine Learning Engineer, embedded in the Fully Autonomous Underwriting (FAU) team. This is a high-ownership, high-ambiguity role. There is no existing ML platform to inherit, no established model registry to maintain. You will build those things. You have the opportunity to define the ML function from the ground up at a company building something genuinely new in a large, underserved market
You will work directly with underwriters to deeply understand the domain, and translate that understanding into ML systems that get meaningfully better over time. You will own the full ML lifecycle – from data through to production – and be the connective tissue between the domain expertise that exists in the business and the systems we’re building to scale it.
WHAT YOU’LL DO
This is an end-to-end ML role. You will own the full lifecycle from raw data through to production systems, and work closely with underwriters, engineers, and product to advance FAU through its autonomy levels.
- Design, build, and ship ML systems that power autonomous underwriting decisions in production
- Build and close the feedback loops that turn human underwriter behavior into training signal and compounding model improvement
- Develop confidence scoring and evaluation frameworks that define when the system is ready to take on more autonomy and when to step back
- Work with large language models to build reliable, auditable, and improvable agentic workflows across the underwriting lifecycle
- Partner directly with underwriters to extract domain knowledge, validate outputs, and earn the trust required to expand the system’s operating domain
- Contribute to the observability, monitoring, and guardrail infrastructure that keeps AI underwriting safe as autonomy scales
WHO YOU ARE
Required
- 4+ years of industry experience building and shipping ML systems end-to-end, from raw data to production models, including experience with model deployment platforms (e.g., AWS Sagemaker)
- Experience finetuning SLMs/LLMs, with a preference for experience using techniques like RLHF, DPO, or LoRA.
- Deep proficiency in Python and modern ML frameworks (PyTorch, HuggingFace, Tensorflow, OpenAI Gym/Gymnasium or similar)
- Experience with LLMs in production: prompt engineering, structured outputs, tool use, evaluation, and cost/latency tradeoffs
- Experience building reliable models with limited labeled data, including synthetic data generation, data augmentation, or similar techniques"
- Strong evaluation instincts: you know how to define what ‘better’ means before you build, not after
- Comfort with ambiguity, highly autonomous, and a bias toward building something real over architecting som