FOUNDING MACHINE LEARNING ENGINEER

Shepherd Insurance Agency; San Francisco (CA); On-site; USD 180,000 - 220,000; Full time

Job summary

Shepherd Insurance Agency is seeking a Machine Learning Engineer to join the Fully Autonomous Underwriting (FAU) team. You will own the ML lifecycle end-to-end and build the platform from the ground up, partnering with underwriters to scale AI‑driven underwriting in a large, underserved market.

You will design and ship ML systems in production, implement feedback loops, and develop confidence scoring to determine autonomy levels. Expect a highly autonomous role in a fast‑moving business context.

Qualifications

• 4+ years of industry experience shipping ML systems end-to-end, including deployment platforms (e.g., AWS SageMaker).
• Experience fine-tuning LLMs, with RLHF, DPO, or LoRA.
• Deep Python proficiency and ML frameworks (PyTorch, HuggingFace, TensorFlow).
• Production LLM experience: prompt engineering, outputs, and cost/latency trade-offs.
• Ability to work with limited labeled data and synthetic data generation.
• Strong evaluation instincts: define 'better' before building.
• Excellent collaboration with non-technical underwriters.

Responsibilities

• Design, build, and ship ML systems powering autonomous underwriting in production.
• Build feedback loops turning human underwriter behavior into training signals.
• Develop confidence scoring and evaluation frameworks for autonomy thresholds.
• Work with large language models to create auditable, improvable workflows.
• Partner with underwriters to extract domain knowledge and validate outputs.
• Contribute observability, monitoring, and guardrail infra for safe scaling.

Skills

End-to-end ML systems

Collaboration with underwriters

Evaluation instincts

Ambiguity tolerance

Model evaluation frameworks

Production ML lifecycle

Education

MS/PhD in quantitative field

Tools

SageMaker

PyTorch

HuggingFace

TensorFlow

OpenAI Gym

Job description

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.

Responsibilities

• 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.

Required Qualifications

• 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 fine‑tuning SLMs/LLMs, with a preference for 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 trade‑offs.
• 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, Mathematics, Physics).

Benefits

• 100% contribution to top‑tier health, dental, and vision.
• Fertility benefits and family building support.
• Unlimited PTO.
• Daily lunches, dinners, and snacks.
• Flexible offices in SF, NYC, Dallas‑Fort Worth, Chicago and LA.
• Professional development including leadership coaching.
• Competitive 401(k) plan.
• Dog‑friendly office.
• Compensation Range: $180K - $220K.