Machine Learning Engineer

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

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

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)

📚 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