About Baselayer: With experience across far-ranging banks, Fortune 500 tech companies, fintech unicorns, and AI experts, Baselayer is built by financial institutions, for financial institutions. Started in 2023 by founders Jonathan Awad and Timothy Hyde, Baselayer has raised $20 million and achieved $2 million in ARR faster than any other identity company. Today, over 2,000 financial institutions and government agencies are customers, revolutionizing fraud prevention and compliance. Check out their press release here: About You You want to learn from the best, get hands-on experience, and reach your potential. You're motivated by excellence and eager to work on cutting-edge AI solutions as a machine learning engineer. 1-3 years of experience in machine learning development, with Python and ML models Comfort working with large-scale data and optimizing performance Strong foundation in AI/ML fundamentals, especially LLMs, with a desire to experiment Prioritize responsible AI practices and model governance, especially in regulated environments like KYC/KYB Attention to detail and pride in writing clean, maintainable code Ability to work independently in a high-trust environment across different levels of abstraction Problem-solving skills and confidence navigating unknowns Proactive, self-starting attitude in dynamic settings Intelligence and pride in your models Receptive to feedback and committed to continuous improvement Responsibilities Model Development & Integration: Build and maintain scalable ML models integrated with various data sources for autonomous agents in the GTM space. ML System Design: Architect core ML services supporting KYC/KYB processes, leveraging knowledge graphs and LLMs for dynamic use cases. Data Processing & Feature Engineering: Develop robust data pipelines for feature extraction and transformation, focusing on scalability and performance. Advanced ML Techniques: Implement state-of-the-art techniques like RLHF and LoRA to enhance LLMs within identity use cases. ML Infrastructure: Build and maintain infrastructure for training, evaluation, and deployment of models. Model Governance & Compliance: Ensure ML systems meet standards for fairness, explainability, and compliance, especially KYC/KYB regulations. Performance Optimization: Optimize inference and training processes for efficiency and reliability. Experimentation & Evaluation: Conduct experiments, debug, monitor, and improve ML architectures for diverse data and use cases. Benefits Hybrid in SF, in-office 3 days/week Flexible PTO, healthcare, 401K Smart, genuine, ambitious team Start date: May Salary Range: $150k – $225k + Equity (0.05% – 0.25%) Additional Details Seniority level: Entry level Employment type: Full-time Job function: Engineering and IT Industries: Technology, Internet #J-18808-Ljbffr Baselayer
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