Founding Machine Learning Engineer
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.
About the job
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
Skills
Python, PyTorch, Huggingface, TensorFlow, Aws Sagemaker, LLMs, RLHF, Dpo, Lora, Prompt Engineering, NLP, LangChain, LangGraph, SQL, dbt
Similar jobs
ML Engineering jobs
Mirage
New York, NY
Build and deploy agentic systems that power AI-driven creative video workflows. The role requires 5+ years of experience, production ML or agentic pipeline development, context engineering, and expertise in evaluation and agent infrastructure.
Mirage
New York, NY
Build and advance agentic machine-learning systems for multimodal creative tasks, with a focus on video understanding, reasoning, control, and tool use. The role requires strong production ML or agent-pipeline experience and deep knowledge of modern LLM techniques.
Taste Labs
San Francisco, CA
Build evaluation methods, RL environments, agent tooling, and scalable infrastructure that make subjective qualities such as design and taste measurable for frontier AI models. The role requires experience with evaluations, RL environments, ML or post-training, plus strong backend engineering skills.
Mirage
New York, NY
Build and scale generative video and multimodal models, optimizing training and inference for efficiency, throughput, and ultra-low latency. The role requires deep learning systems expertise, strong PyTorch/CUDA experience, and the ability to move research models into production.
Earnin
Mountain View, CA
Build production-grade AI agents, evaluation infrastructure, and developer tooling that make AI-assisted engineering faster, safer, and reusable across teams. The role requires software engineering experience, platform or internal developer-product experience, and hands-on expertise with LLM integration and orchestration.