Machine Learning Engineer

Responsibilities

• check_circle Model Development & Integration: Build and maintain ML models and integrate them with various data sources, ensuring scalability, high performance, and adaptability for autonomous agents in the GTM space
• check_circle ML System Design: Architect and design core ML services that support KYC/KYB processes, leveraging knowledge graphs and LLMs for dynamic use cases
• check_circle Data Processing & Feature Engineering: Develop and maintain robust data pipelines for feature extraction and transformation, focusing on scalability and performance when handling large-scale, high-dimensional data
• check_circle Advanced ML Techniques: Implement and experiment with state-of-the-art techniques including reinforcement learning from human feedback (RLHF) and parameter-efficient fine-tuning methods (e.g., LoRA) to improve LLMs for specific use cases within the identity space
• check_circle ML Infrastructure: Build and maintain infrastructure for model training, evaluation, and deployment, creating a scalable platform foundation for continued innovation
• check_circle Model Governance & Compliance: Ensure ML systems meet industry standards for fairness, explainability, and compliance, particularly around KYC/KYB regulations
• check_circle Performance Optimization: Implement optimizations for model inference and training, ensuring ML services can efficiently process identity data while maintaining reliability
• check_circle Experimentation & Evaluation: Design and conduct experiments to evaluate model performance, debug issues, and monitor ML services, while continuously improving architectures to handle diverse data and use cases

Benefits

• check_circle Hybrid in SF. In office 3 days/week
• check_circle Flexible PTO
• check_circle Healthcare, 401K
• check_circle Smart, genuine, ambitious team

About the company