Founding Machine Learning Engineer

About the role

You will be Shepherd’s first Machine Learning Engineer, embedded in the Fully Autonomous Underwriting (FAU) team. This high‑ownership role involves building the ML platform from scratch and advancing autonomous underwriting from L1 toward L3 and beyond.

Key responsibilities

• Design, build, and ship ML systems that power autonomous underwriting decisions in production.
• Develop feedback loops that turn underwriter behavior into training signals for continuous model improvement.
• Create confidence scoring and evaluation frameworks to determine when the system can assume more autonomy.
• Work with large language models to build reliable, auditable agentic workflows across the underwriting lifecycle.
• Collaborate directly with underwriters to extract domain knowledge, validate outputs, and earn trust.

Required profile

• 4+ years of experience building and shipping end‑to‑end ML systems, including model deployment on platforms such as AWS SageMaker.
• Hands‑on experience fine‑tuning SLMs/LLMs using techniques like RLHF, DPO, or LoRA.
• Deep proficiency in Python and modern ML frameworks (PyTorch, HuggingFace, TensorFlow, OpenAI Gym/Gymnasium).
• Proven ability to work with limited labeled data through synthetic data generation or augmentation.
• Strong evaluation instincts and comfort operating in ambiguous, high‑autonomy environments.

Required skills

• Python
• PyTorch
• HuggingFace
• TensorFlow
• OpenAI Gym / Gymnasium
• AWS SageMaker
• LLM fine‑tuning (RLHF, DPO, LoRA)
• Prompt engineering and structured output generation
• Synthetic data generation and data augmentation

What we offer

• Premium healthcare with 100% employer contribution
• Fertility benefits and family‑building support
• Unlimited PTO and flexible time off
• Daily lunches, dinners, and snacks
• Offices in San Francisco, New York, Dallas‑Fort Worth, Chicago, and Los Angeles
• Professional development and leadership coaching
• Competitive 401(k) plan
• Dog‑friendly office environment