Machine Learning Engineer

Baselayer•San Francisco, CA

14d•$145,000 - $225,000•Hybrid

About The Position

Trusted by 2,200+ financial institutions, Baselayer is the intelligent business identity platform that helps verify any business, automate KYB, and monitor real-time risk. Baselayer’s B2B risk solutions & identity graph network leverage state & federal government filings and proprietary data sources to prevent fraud, accelerate onboarding, and lower credit losses. 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 not just doing it for the win—you're doing it because you have something to prove and want to be great. You are looking to be an impeccable machine learning engineer working on cutting-edge AI solutions.

Requirements

You have 4-8 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
Problem-solver who navigates the unknown confidently
Proactive self-starter who thrives in dynamic settings
Incredibly intelligent and clever. You take pride in your models
Highly feedback-oriented. We believe in radical candor and using feedback to get to 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 when handling large-scale, high-dimensional data
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
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 compliance, particularly around KYC/KYB regulations
Performance Optimization: Implement optimizations for model inference and training, ensuring ML services can efficiently process identity data while maintaining reliability
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