Senior ML Engineer: Foundation Models & Scalable Systems

AI Breaking Wire; Mountain View (CA); On-site; USD 200,000 - 350,000; Full time

Benefits offered by this job

Stock grants

Health/dental/vision benefits

Paid time off & parental leave

Professional development

Job summary

Google DeepMind is seeking a Senior Machine Learning Engineer to design, build, and scale foundation-model systems in Mountain View. You will help prototype, optimize, and productionize large-scale ML pipelines, collaborating with researchers and engineers across teams.

The role emphasizes ML engineering excellence, distributed training, and staying at the forefront of AI research. Competitive compensation includes bonus and stock grants, with comprehensive benefits.

Qualifications

• Master's or PhD in Computer Science, Machine Learning, or related field, or equivalent practical experience.
• 5+ years of experience in machine learning engineering for large-scale systems.
• Strong programming in Python; experience with TensorFlow or PyTorch.
• Experience with distributed training, model optimization, and MLOps.
• Deep understanding of foundation models including LLMs and diffusion models.
• Excellent problem-solving and debugging skills.

Responsibilities

• Design, implement, and optimize large-scale ML systems and infrastructure for foundation models.
• Develop and refine training pipelines, evaluation frameworks, and deployment strategies.
• Collaborate with researchers to bring novel ML algorithms into production.
• Troubleshoot and resolve complex ML system challenges.
• Mentor junior engineers and promote ML engineering best practices.
• Stay current with research trends in LLMs and generative AI.

Skills

ML engineering

Foundation models

LLMs

Python programming

Distributed systems

MLOps

Education

Master's or PhD in CS/ML

Tools

TensorFlow

PyTorch

Job description

Google DeepMind is seeking a Senior Machine Learning Engineer to design, build, and scale foundation-model systems in Mountain View. You will help prototype, optimize, and productionize large-scale ML pipelines, collaborating with researchers and engineers across teams.

The role emphasizes ML engineering excellence, distributed training, and staying at the forefront of AI research. Competitive compensation includes bonus and stock grants, with comprehensive benefits.