Staff Machine Learning Engineer

Palo Alto, California

Machine Learning

Permanent

Our client, an AI-driven organization, are hiring a Staff Machine Learning Engineer to join their team in California. The successful candidate will work with the latest cutting-edge technologies to help define the company’s data science strategy, design and implement state-of-the-art machine learning models, and play a key role in advancing their Agentic AI products.

Responsibilities

Develop and deploy cutting-edge machine learning solutions to drive personalized customer experiences and business growth.
Architect and refine intelligent, AI-powered agents capable of executing complex, multi-step workflows across diverse platforms.
Enhance and scale high-performance ML models that deliver real-time decisions to tens of millions of users.
Own the end-to-end evaluation process, including offline metrics and A/B testing, to ensure continuous model performance improvements.
Explore and interpret vast datasets to extract meaningful insights that inform product and business strategies.
Share data-driven recommendations and partner cross-functionally with engineering, product and business teams to bring solutions to life.
Stay up to date with latest trends by actively engaging with the latest AI research, tools and industry developments.

Skillset

PhD or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence similar.
At least 7 years of hands-on experience developing, deploying and optimizing ML models in real-world production settings.
Deep expertise with leading ML frameworks such as TensorFlow, PyTorch or JAX.
Strong command of Python and its data science ecosystem, including NumPy, Pandas and SciPy.
Proven experience working with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) and information retrieval technologies.
A solid foundation in recommender systems, feed ranking and algorithmic optimization.
Applied knowledge of causal inference methods and experimental design best practices.
Familiarity with cloud infrastructure and the ability to work with large, complex datasets at scale.