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Blee is an AI-first marketing content compliance platform trusted by PayPal, NerdWallet, and Brex. Founded by an ex-Adobe marketing ops leader and former lawyer, the company is YC-backed and defining a new category across Fintech, Financial Services, CPG, Travel, and Life Sciences.
In this Applied Scientist role, you will architect and implement the next generation of intelligent compliance systems. You'll design frameworks where multiple agents—powered by custom and foundational models—work together to surface, track, and explain compliance flags. This is a hands-on, production-focused position where you'll build and optimize applied AI/ML pipelines, work across model fine-tuning, prompt engineering, evaluation frameworks, and customer-specific AI configurations.
Key responsibilities include:
- Architect multi-agent systems that integrate foundational LLMs and custom-trained models to power intelligent compliance workflows
- Build and optimize AI/ML pipelines including model fine-tuning, prompt engineering, and evaluation frameworks
- Ensure explainability and traceability by building mechanisms for transparent flagging, where every compliance flag is traceable to prior rules, examples, or data assets
- Automate compliance rule translation by building systems that transform compliance rules into agent-ready logic paths
- Design, train, and fine-tune models to improve compliance accuracy and adaptability using user feedback and outcomes data
- Design model evaluation and monitoring infrastructure to ensure reliability and auditability across customer deployments
- Collaborate cross-functionally with Product Engineers to integrate AI features via APIs and data pipelines, with model training engineers to incorporate feedback loops, and with compliance users to translate complex rules into agent workflows
- Stay current on AI/ML advances and prototype new techniques for the platform
You'll work closely with Blee's AI and engineering leadership, collaborating daily with product engineers and implementation managers. The company values in-person collaboration with offices in San Francisco and New York, expecting 2-3 days per week in the office for Bay Area or NY-based candidates.
Requirements:
- 5+ years of applied ML/AI engineering experience
- Strong knowledge of agent-based architectures and orchestration frameworks
- Strong Python proficiency with modern software engineering practices (APIs, microservices, distributed systems)
- Experience with ML frameworks (PyTorch, TensorFlow, Hugging Face)
- LLM experience including fine-tuning, prompt engineering, RAG, and evaluation
- Background in data preprocessing, feature engineering, and training pipelines
- Production ML experience: model serving, monitoring, A/B testing, data pipelines
- Strong communication skills and ability to work cross-functionally
- Based in Bay Area or NY with willingness to come into office 2-3 days per week
