Applied Scientist

Blee•San Francisco, CA

1d•$150,000 - $275,000

About The Position

Blee is hiring an Applied Scientist specializing in agent system design to architect and implement intelligent compliance systems. The role involves designing frameworks in which multiple agents, powered by foundational and custom-trained models, surface, track, and explain compliance flags. You will build and optimize applied AI/ML pipelines, work on model fine-tuning, prompt engineering, evaluation frameworks, and customer-specific AI configurations, and use user feedback to train models that deliver more targeted compliance recommendations. The Applied Scientist will work closely with Blee’s AI and engineering leadership, product engineers, implementation managers, and compliance users. The position is suited to a hands-on, customer-outcome-oriented technical contributor who can work across engineering, product, compliance, and customer-facing teams. Blee values in-person collaboration, with offices in San Francisco and New York, while considering remote opportunities for the right candidate.

Requirements

At least 5 years of applied ML/AI engineering experience.
Strong knowledge of agent-based architectures and orchestration frameworks.
Strong Python skills and proficiency with modern software engineering practices, including APIs, microservices, and distributed systems.
Experience with ML frameworks such as PyTorch, TensorFlow, and Hugging Face.
LLM experience, including fine-tuning, prompt engineering, RAG, and evaluation.
Background in data preprocessing, feature engineering, and training pipelines.
Production ML experience, including model serving, monitoring, A/B testing, and data pipelines.
Strong communication skills and ability to work cross-functionally.
Based in the Bay Area or New York and willing to come into the office 2–3 days a week.

Responsibilities

Design and implement multi-agent systems that integrate foundational LLMs and custom-trained models for compliance workflows.
Build and optimize AI/ML pipelines, including model fine-tuning, prompt engineering, and evaluation frameworks.
Develop mechanisms that make compliance flags transparent and traceable to rules, examples, or data assets.
Build systems that translate compliance rules into agent-ready logic paths.
Design, train, and fine-tune models using user feedback and outcomes data to improve compliance accuracy and personalization.
Design model evaluation and monitoring infrastructure to support reliability and auditability across customer deployments.
Collaborate with product engineers to integrate AI features through APIs, data pipelines, and frontend systems.
Partner with model training engineers to incorporate feedback loops into system design.
Work with compliance users to translate complex rules into agent workflows.
Prototype and apply new AI/ML techniques to the platform.