# Founding Machine Learning Engineer

**Company:** [Shepherd](https://hotfix.jobs/companies/shepherd)
**Location:** San Francisco, CA, New York, NY, Dallas-Fort Worth, TX, Chicago, IL, Los Angeles, CA
**Role:** ML Engineering
**Salary:** $180k – $220k/yr
**Experience:** 4+ years
**Skills:** Python, PyTorch, Huggingface, TensorFlow, Aws Sagemaker, LLMs, RLHF, Dpo, Lora, Prompt Engineering, NLP, LangChain, LangGraph, SQL, dbt
**Posted:** 2026-04-02

> Builds end-to-end ML systems for autonomous insurance underwriting, finetuning LLMs, closing feedback loops with underwriter data, and deploying production models. Requires 4+ years ML experience, Python proficiency, and production LLM expertise.

## Job Description

## What You’ll Do
- 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

## Who You Are

### Required
- 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

### 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, Math, Physics)

## Benefits
- Premium Healthcare: 100% contribution to top-tier health, dental, and vision
- Fertility benefits and family building support
- Unlimited PTO
- Daily lunches, dinners, and snacks
- 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

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