Optimization Research Engineer - Large-Scale ML/AI (Equity)

Google LLC; San Francisco (CA); On-site; USD 147,000 - 210,000; Full time

Job summary

Google DeepMind is seeking an Optimization Research Engineer to tackle large-scale optimization problems and develop ML-driven solutions. You will prototype algorithms, run large experiments, and integrate with TensorFlow to push state-of-the-art in optimization and AI.

The role emphasizes research alignment, collaboration, and publishing results while contributing to real-world infrastructure challenges and scalable performance improvements.

Qualifications

• Bachelor’s degree or equivalent practical experience.
• 2 years working with large-scale optimization models.
• 2 years of experience with solvers and problem types, including integer, nonlinear, and stochastic programs.
• Experience with machine learning, TensorFlow, and in software engineering.
• Experience building machine learning models on novel, real datasets.

Responsibilities

• Tackle large-scale optimization problems for power grids.
• Develop and apply machine learning models using TensorFlow on real datasets.
• Collaborate with research teams across Google DeepMind to publish findings.
• Design experiments and prototypes to improve optimization pipelines.

Skills

Machine learning

TensorFlow

Software engineering

ML on real data

Education

Bachelor’s degree or equivalent practical experience

Tools

Solvers

Job description

Google DeepMind is seeking an Optimization Research Engineer to tackle large-scale optimization problems and develop ML-driven solutions. You will prototype algorithms, run large experiments, and integrate with TensorFlow to push state-of-the-art in optimization and AI.

The role emphasizes research alignment, collaboration, and publishing results while contributing to real-world infrastructure challenges and scalable performance improvements.