Machine Learning Engineer
Shepherdinsurance
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Benefits offered by this job
Job summary
Qualifications
- 4+ years of industry experience building and shipping ML systems end-to-end.
- Experience finetuning SLMs/LLMs using techniques like RLHF or DPO.
- Familiarity with document parsing or NLP on unstructured business documents.
Responsibilities
- Design and build ML systems for autonomous underwriting decisions.
- Work closely with underwriters to understand the domain.
- Contribute to observability and monitoring infrastructure.
Skills
Education
Tools
Job description
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.
The infrastructure behind the AI boom — data centers, semiconductor fabs, renewable energy assets — has to be built and insured. But traditional carriers weren't built for this speed:
- Complex commercial construction projects routinely wait weeks for a single quote
- Legacy carriers rely on static applications and disconnected systems
- Brokers chase carriers through calls, emails, and resubmissions
We built Shepherd to solve that. Our AI performs the same underwriting workflows in seconds, and integrates real-time data from construction technology partners — Procore, Autodesk, OpenSpace, DroneDeploy, and others — to see risk as it actually exists, not just as it was reported on a static form.
We're pursuing the most ambitious technical vision in commercial insurance: fully autonomous underwriting. We're closing in on the first fully agentic submission in the industry — email in, price out, no human intervention until the last mile.
With Shepherd, safety, speed, and quality no longer trade off against one another — they compound. We're building:
- Faster decisions
- Smarter, more accurate pricing
- Better risk outcomes for insureds who invest in safer practices
We're not just modernizing insurance products. We're building the risk infrastructure for the next generation of financial services.
In March 2026, Shepherd raised a $42M Series B — bringing total funding to over $60M — led by Intact Private Capital, the investment arm of one of the largest insurers in the world. Intact is not only our lead investor but also a carrier partner, a testament to the confidence the incumbent industry has in what we're building. Our investors:
- Intact Private Capital
- Spark Capital
- Costanoa Ventures
- Y Combinator
- Susa Ventures
- And several others
We're a team of technologists and insurance enthusiasts, bridging the two worlds together. Check out our About page to learn more.
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.
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.
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
Required
- 4+ years of industry experience building and shipping ML systems end-to-end, from raw data to production models
- 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 something perfect
- Excellent collaboration skills. You will spend significant time with non-technical underwriters and need to earn their trust
Nice to Have
- Familiarity with document parsing, information extraction, or NLP on unstructured business documents
- Background in insurance, finance, or other high-stakes structured domains where model errors have real consequences
- Experience with agentic frameworks or multi-step LLM orchestration (LangChain, LangGraph, or custom)
- Confidence calibration experience: isotonic regression, Platt scaling, or similar techniques
- TypeScript proficiency. Our platform is TypeScript-heavy and cross-functional contribution is valued
- Familiarity with data pipelines: SQL, dbt, Spark, or equivalent
- MS or PhD in a quantitative field (ML/AI, Statistics, Math, Physics)
🏥 Premium Healthcare
100% contribution to top-tier health, dental, and vision
🥕 Fertility benefits and family building support
🏖️ Unlimited PTO
Flexibility to take the time off, recharge, and perform
🥗 Daily lunches, dinners, and snacks
We work together, and enjoy meals together too
🖥️ SF, NYC, Dallas-Fort Worth, Chicago and LA Offices
📚 Professional Development
Access to premium coaching, including leadership development
🏦 Competitive 401(k) Plan
🐶 Dog-friendly office
Plenty of dogs to play with and make friends with in the SF office
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