Machine Learning Engineer

Baselayer is built by financial institutions, for financial institutions. Started in 2023 by experienced founders, Baselayer works with banks, Fortune 500 tech cos, fintechs, and AI experts to revolutionize fraud prevention and compliance. Baselayer has raised funding and earned notable ARR as part of its growth narrative. About You You want to learn from the best of the best, get your hands dirty, and put in the work to hit your full potential. You're aiming to be an impeccable machine learning engineer working on cutting-edge AI solutions. You have 1-3 years of experience in machine learning development, working with Python and building ML models You're comfortable working with large-scale data and enjoy optimizing performance for computationally intensive ML systems You have a strong foundation in AI/ML fundamentals, particularly with LLMs, and are eager to experiment with emerging techniques You prioritize responsible AI practices and model governance, especially in regulated environments like KYC/KYB You have a keen eye for detail and take pride in writing clean, maintainable code while optimizing for model performance You thrive in a high-trust, ownership-focused environment and are comfortable working across different levels of abstraction You are a problem-solver who navigates the unknown confidently You are a proactive self-starter who thrives in dynamic settings You are highly intelligent and clever, with pride in your models You are highly feedback-oriented and value candor to reach the next level Responsibilities 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 ML System Design: Architect and design core ML services that support KYC/KYB processes, leveraging knowledge graphs and LLMs for dynamic use cases Data Processing & Feature Engineering: Develop and maintain robust data pipelines for feature extraction and transformation, focusing on scalability and performance with large-scale, high-dimensional data Advanced ML Techniques: Implement and experiment with state-of-the-art techniques including RLHF and parameter-efficient fine-tuning methods (e.g., LoRA) to improve LLMs for identity-related use cases ML Infrastructure: Build and maintain infrastructure for model training, evaluation, and deployment, creating a scalable platform foundation for continued innovation Model Governance & Compliance: Ensure ML systems meet industry standards for fairness, explainability, and regulatory compliance (KYC/KYB) Performance Optimization: Optimize model inference and training for efficient processing of identity data while maintaining reliability Experimentation & Evaluation: Design and conduct experiments to evaluate model performance, debug issues, and monitor ML services, while improving architectures for diverse data and use cases Hybrid in SF. In-office 3 days/week Flexible PTO Collaborate with a smart, genuine, ambitious team Salary & Benefits Salary Range: $150k - $225k + Equity - 0.05% - 0.25% Seniority level Entry level Employment type Full-time Job function Engineering and Information Technology Industries Technology, Information and Internet San Francisco, CA - location details omitted here for refinement; original postings have been removed to keep the description concise and job-focused. #J-18808-Ljbffr

Created: 2025-10-01