6 months ago
San Francisco, CA, USAMid Level
Base Salary
$180k - $220k/yr
Responsibilities
- Design, build, and ship production ML systems powering autonomous underwriting decisions
- Create feedback loops that convert underwriter behavior into training signals and ongoing model improvement
- Develop confidence scoring and evaluation frameworks to determine when the system should increase autonomy or defer to humans
- Build reliable, auditable, and improvable LLM-based agentic workflows across the underwriting lifecycle
- Work directly with underwriters to extract domain knowledge, validate outputs, and expand the system’s operating domain
- Contribute to observability, monitoring, and guardrail infrastructure for safe AI underwriting
- Own the ML lifecycle from raw data through production and collaborate with underwriters, engineers, and product
Requirements
- 4+ years of industry experience building and shipping end-to-end ML systems, including model deployment platforms such as AWS Sagemaker
- Experience fine-tuning SLMs or LLMs, preferably using RLHF, DPO, or LoRA
- Deep proficiency in Python and modern ML frameworks including PyTorch, HuggingFace, TensorFlow, and OpenAI Gym/Gymnasium or similar
- Production experience with LLMs, including prompt engineering, structured outputs, tool use, evaluation, and cost and latency tradeoffs
- Experience building reliable models with limited labeled data using synthetic data generation, data augmentation, or similar techniques
- Strong evaluation judgment and ability to define measurable improvement criteria
- Comfort working autonomously in ambiguous, high-ownership environments
- Excellent collaboration skills and ability to build trust with non-technical underwriters
- Familiarity with document parsing, information extraction, or NLP for unstructured business documents
- Background in insurance, finance, or another high-stakes structured domain
- Experience with agentic frameworks or multi-step LLM orchestration such as LangChain, LangGraph, or custom systems
- Experience with confidence calibration techniques such as isotonic regression or Platt scaling
- TypeScript proficiency
- Familiarity with data pipelines using SQL, dbt, Spark, or equivalent
- An MS or PhD in a quantitative field such as ML/AI, statistics, mathematics, or physics is preferred
Benefits
- 100% employer contribution to health, dental, and vision coverage
- Fertility benefits and family-building support
- Unlimited paid time off
- Daily lunches, dinners, and snacks
- Offices in San Francisco, New York City, Dallas-Fort Worth, Chicago, and Los Angeles
- Professional development and premium coaching, including leadership development
- Competitive 401(k) plan
- Dog-friendly San Francisco office
Tech Stack
Categories
About Shepherd
Shepherd is a technology-driven managing general underwriter that provides casualty insurance and risk management tools for commercial construction and other high‑hazard industries. It sells through brokers and pairs underwriting with software to help contractors manage safety and claims. Founded in 2021 and headquartered in San Francisco, Shepherd is privately held and partners with carriers including Intact; it announced a Series B round in 2026 led by Intact Private Capital.