Founding Machine Learning Engineer

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 with no pre‑existing ML platform or model registry. You will build those from the ground up and work directly with underwriters to translate domain knowledge into ML systems that continuously improve.

• 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 signals 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.

• 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 Sage Maker).
• Experience fine‑tuning SLMs/LLMs, with a preference for techniques like RLHF, DPO, or LoRA.
• Deep proficiency in Python and modern ML frameworks (PyTorch, Hugging Face, Tensor Flow, 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.

• 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 (Lang Chain, Lang Graph, or custom).
• Confidence calibration experience: isotonic regression, Platt scaling, or similar techniques.
• Type Script proficiency. Our platform is Type Script‑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.
• Daily lunches, dinners, and snacks.
• Location options: 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 with plenty of dogs to play with and make friends with in the SF office.

Compensation Range: $180K – $220K