Machine Learning Engineer

About Baselayer: With experience across far-ranging banks, Fortune 500 tech co’s, fintech unicorns 🦄, and AI experts, Baselayer is built by financial institutions, for financial institutions. Started in 2023 by experienced founders Jonathan Awad and Timothy Hyde, Baselayer has raised $20 Million and hit $2 Million in ARR faster than any other identity company in history. Today more than 2,000 Financial Institutions and Government Agency customers later, Baselayer is revolutionizing the way businesses approach fraud prevention and compliance. 🏆

Check out their press release here → https://baselayerhq.com/press/

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.

• 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
• 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

Benefits:

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

Start date: June