Machine Learning Engineer

Noveltechservices; San Francisco (CA); On-site; USD 90,000 - 150,000; Full time

Job summary

An innovative firm is seeking a Machine Learning Engineer to enhance identity insights using cutting-edge technologies. This role involves developing and integrating ML models, focusing on scalability and performance, while ensuring compliance with KYC/KYB regulations. The ideal candidate will have a strong foundation in Python and ML, with a passion for experimenting with advanced techniques like RLHF and LoRA. Join a dynamic team dedicated to responsible AI practices and make a significant impact in the fight against B2B fraud. If you thrive in a collaborative, ownership-driven environment, this opportunity is perfect for you.

Qualifications

• 1-3 years of experience in machine learning development.
• Strong coding skills in Python and ML modeling.
• Commitment to responsible AI practices.

Responsibilities

• Build and maintain ML models integrating with various data sources.
• Architect core ML services supporting KYC/KYB processes.
• Develop data pipelines for feature extraction and transformation.

Skills

Python

Machine Learning

Large Language Models (LLMs)

Data Processing

Model Optimization

Problem Solving

Tools

PyTorch

TensorFlow

Google Cloud Platform

Job description

Machine Learning Engineer

Our client is delivering the most comprehensive identity insights. Their 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 data-sharing entity between financial institutions.

We are looking for an ML engineer to leverage LLMs to enhance business profiles with fragmented information available across the web.

Technologies:

• LLMs (using OpenAI, Anthropic, etc.)
• Python
• NN libraries (PyTorch, TensorFlow)
• Google Cloud Platform

Responsibilities:

1. Model Development & Integration: Build and maintain ML models, integrating them with various data sources to ensure scalability, high performance, and adaptability for autonomous agents in the GTM space.
2. ML System Design: Architect core ML services supporting KYC/KYB processes, leveraging knowledge graphs and LLMs for dynamic use cases.
3. Data Processing & Feature Engineering: Develop data pipelines for feature extraction and transformation, focusing on scalability and performance with large-scale, high-dimensional data.
4. Advanced ML Techniques: Implement and experiment with techniques like RLHF and LoRA to improve LLMs for identity-related use cases.
5. ML Infrastructure: Build and maintain infrastructure for training, evaluation, and deployment of models, ensuring scalability.
6. Model Governance & Compliance: Ensure systems meet standards for fairness, explainability, and compliance with KYC/KYB regulations.
7. Performance Optimization: Optimize model inference and training for efficiency and reliability.
8. Experimentation & Evaluation: Conduct experiments to evaluate and improve model performance, debugging issues, and monitoring services.

Qualifications:

• 1-3 years of experience in machine learning development, with proficiency in Python and ML modeling.
• Strong coding skills, especially in writing clean, maintainable, and optimized code.
• Comfort with large-scale data processing and performance optimization.
• Solid foundation in AI/ML fundamentals, particularly with LLMs, and eagerness to experiment with emerging techniques.
• Commitment to responsible AI practices and model governance, especially in regulated environments.
• Attention to detail and pride in model quality and performance.
• Ability to work in a high-trust, ownership-driven environment across different abstraction levels.
• Problem-solving skills and confidence in navigating unknowns.
• Proactive, feedback-oriented, and adaptable to dynamic settings.