Founding Machine Learning Engineer

The RoleYou 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 DoThis 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 productionBuild and close the feedback loops that turn human underwriter behavior into training signal and compounding model improvementDevelop confidence scoring and evaluation frameworks that define when the system is ready to take on more autonomy and when to step backWork with large language models to build reliable, auditable, and improvable agentic workflows across the underwriting lifecyclePartner directly with underwriters to extract domain knowledge, validate outputs, and earn the trust required to expand the system's operating domainContribute to the observability, monitoring, and guardrail infrastructure that keeps AI underwriting safe as autonomy scalesWho You AreRequired4+ 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 tradeoffsExperience 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 afterComfort with ambiguity, highly autonomous, and a bias toward building something real over architecting something perfectExcellent collaboration skills. You will spend significant time with non-technical underwriters and need to earn their trustNice to HaveFamiliarity with document parsing, information extraction, or NLP on unstructured business documentsBackground in insurance, finance, or other high-stakes structured domains where model errors have real consequencesExperience with agentic frameworks or multi-step LLM orchestration (LangChain, LangGraph, or custom)Confidence calibration experience: isotonic regression, Platt scaling, or similar techniquesTypeScript proficiency. Our platform is TypeScript-heavy and cross-functional contribution is valuedFamiliarity with data pipelines: SQL, dbt, Spark, or equivalentMS or PhD in a quantitative field (ML/AI, Statistics, Math, Physics)