ML Performance Optimization Software Engineer

Posted 12 Days Ago
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New York, NY
In-Office
Expert/Leader
Artificial Intelligence
The Role
Lead a multi-disciplinary team to drive software evolution for machine learning accelerators, collaborating closely with hardware teams and managing virtual relationships.
Summary Generated by Built In
Snapshot

At Google DeepMind, we've built a unique culture and work environment where long-term ambitious research can flourish. We are seeking a highly motivated and experienced ML Software Engineering Manager to join our HW-SW Co-design team and drive groundbreaking advances for machine learning acceleration.

About us

Artificial Intelligence could be one of humanity’s most useful inventions. At Google DeepMind, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence. We use our technologies for widespread public benefit and scientific discovery, and collaborate with others on critical challenges, ensuring safety and ethics are the highest priority.

About you

We seek out individuals who thrive in ambiguity and who are willing to help out with whatever moves our HW-SW co-design project forward. We regularly need to invent novel solutions to problems, and often change course if our ideas don’t work out, so flexibility and adaptability to work on any project is a must. We value strong leadership, technical depth, and a collaborative spirit.

The Role

We are seeking a talented and highly motivated ML Software Engineering Manager to join our GenAI technical infrastructure research team. You will lead a multi-disciplinary team to evolve the software side of our hw-sw co-design project. This role requires a blend of deep technical expertise, strategic thinking, and strong leadership.

Responsibilities:
  • Lead the work of multi-disciplinary ML software engineers, including numerics, performance optimisation, teacher-student learning, and novel model architecture exploration.
  • Closely collaborate with our hardware team to define and drive strategy for next-generation machine learning accelerators.
  • Manage relationships and technical execution across a virtual team that spans both Google and outside partners.
  • Drive the team to deliver high-quality aligned to tight schedules.
Minimum Qualifications:
  • Bachelor's degree in Electrical Engineering, Computer Science, or equivalent practical experience.
  • 10+ years of experience in ASIC design and development.
  • Proven track record of technical leadership and successfully delivering complex silicon projects (tape-outs) to production.
  • Deep expertise in at least one core silicon discipline (e.g., RTL, PD, DV) and strong familiarity with the entire ASIC flow.
  • Experience with managing silicon vendors and other external partners.
Preferred Qualifications:
  • Master's or Ph.D. in a related field.
  • Experience leading and managing teams across the full silicon development cycle, from RTL to bringup.
  • Experience with high-performance compute IPs (e.g., GPUs, ML accelerators).
  • Knowledge of high-performance and low-power architectures for ML acceleration.
  • Excellent communication, and leadership skills.

Top Skills

Asic Design
Dv
High-Performance Compute Ips
Machine Learning
Ml Accelerators
Pd
Rtl
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The Company
1,218 Employees
Year Founded: 2010

What We Do

We’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence. We use our technologies for widespread public benefit and scientific discovery, and collaborate with others on critical challenges, ensuring safety and ethics are the highest priority.

Our long term aim is to solve intelligence, developing more general and capable problem-solving systems, known as artificial general intelligence (AGI).

Guided by safety and ethics, this invention could help society find answers to some of the world’s most pressing and fundamental scientific challenges.

We have a track record of breakthroughs in fundamental AI research, published in journals like Nature, Science, and more.Our programs have learned to diagnose eye diseases as effectively as the world’s top doctors, to save 30% of the energy used to keep data centres cool, and to predict the complex 3D shapes of proteins - which could one day transform how drugs are invented.

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