AI/ML Engineer

Position Overview

We are seeking 2 talented AI/ML Engineers to join our team in San Francisco, CA. In this full-time role, you will be working onsite to build, deploy, and optimize real-world Machine Learning and Artificial Intelligence models that drive core business products.

The ideal candidate has hands-on experience in the ML lifecycle—from data preprocessing and model development to productionizing and maintaining scalable ML microservices and pipelines.

Key Responsibilities

• ML Model Productionization: Design, train, evaluate, and ship machine learning and data science models into production environments.

• Pipeline & Infrastructure Development: Build, optimize, and maintain scalable data and feature pipelines to support model training, testing, and continuous deployment.

• Algorithm & Framework Integration: Apply statistical models, deep learning techniques, or Generative AI/NLP frameworks to solve complex technical challenges.

• Performance Monitoring & Tuning: Track, monitor, and optimize production ML models for latency, accuracy, scalability, and drift.

• Cross-Functional Collaboration: Partner closely with software engineers, product managers, and data teams to integrate AI capabilities into end-user applications.

Required Qualifications

• Experience: 1+ years of practical experience in Machine Learning, Data Science, or AI engineering with a proven track record of shipping ML systems into production.

• Programming Skills: Strong proficiency in Python and standard ML libraries (e.g., PyTorch, TensorFlow, Scikit-Learn, Pandas, NumPy).

• Software Engineering Fundamentals: Solid understanding of object-oriented programming, software architecture, unit testing, and version control (Git).

• Data & Deployment: Experience working with SQL/NoSQL databases, RESTful APIs, and basic containerization (Docker).

• Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Mathematics, or a related quantitative field.

Preferred Qualifications

• Experience with cloud platforms (AWS, Google Cloud Platform, or Azure) for ML training and deployment.

• Knowledge of MLOps tools (e.g., MLflow, Kubeflow, Weights & Biases) and CI/CD pipelines.

• Exposure to modern LLM frameworks, RAG architectures, or prompt engineering.