Founding Machine Learning Engineer

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 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. 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 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) How we work Shepherd runs on four values. Here's what each one means in this seat. Think big, build big. We exist to protect progress and the industries that rely on it. The work here is aimed at a system that runs on its own, and the roadmap gets sequenced backward from that rather than forward from what's easy. Win together. We rise as one. We support each other, raise the bar, and celebrate collective success. Come set a standard the rest of the team inherits. Cross the aisle. Collaboration wins. We listen deeply, work across boundaries, and prioritize shared success over individual lanes. The best product calls here come from engineers who've sat with underwriters and underwriters who understand where the model breaks, and much of this job is listening closely enough on both sides to make that happen. Go get it. We act with urgency, move with confidence, take smart risks, and push forward with intention. Nobody hands you the roadmap, the data, or the meeting invite. You pull the failing runs, book the time with the underwriters, and decide what matters. Benefits 🏥 Premium Healthcare 100% contribution to health, dental, and vision 🥕 Fertility benefits and family building support 🏖️ Flexible 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