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Posted Apr 2Founding Machine Learning Engineer
San Francisco, United StatesOn-site
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
Requirements
- The infrastructure behind the AI boom — data centers, semiconductor fabs, renewable energy assets — has to be built and insured.
- Our AI performs the same underwriting workflows in seconds, and integrates real-time data from construction technology partners — Procore, Autodesk, OpenSpace, DroneDeploy, and others — to see risk as it actually exists, not just as it was reported on a static form.
- You will build the ML systems that carry us from L1 to L3 and beyond.
- THE ROLE You will be Shepherd’s first Machine Learning Engineer, embedded in the Fully Autonomous Underwriting (FAU) team.
- There is no existing ML platform to inherit, no established model registry to maintain.
- 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.
- WHAT YOU’LL DO This is an end-to-end ML role.
- 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 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.