Machine Learning Engineer

Machine Learning Engineer:

Our client is Delivering the most comprehensive identity insights, our client’s platform equips businesses with fully automated KYB (Know your Business) solutions for Risk, and Fraud management, setting new standards in business verification.

The solution is designed for FS institutions to emphasize their interest in a coordinated effort to mitigate B2B fraud, reduce the risk associated with working with small businesses, and create a centralized, privacy-compliant entity for data-sharing between financial institutions.

Looking for an ML engineer to leverage LLMs to enhance business profiles with fragmented information that exists around the web.

Technologies:

LLMs (using OpenAI/Anthropic/etc)

Python

NN libraries (Pytorch/Tensorflow)

Google Cloud Platform

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

Requirements

You have 1-3 years of experience in machine learning development, working with Python and building ML models

Writing GREAT code!

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