Machine Learning Engineer

Join a team at the forefront of innovation where machine learning meets custom silicon design. As a Machine Learning Engineer within Google's Silicon Organization, you will be part of a forward-thinking team that is reshaping the future of computing by developing custom silicon solutions to power Google’s next-generation hardware devices. These products touch millions of lives globally help us continue to raise the bar.

You will leverage your experience in both physical design and machine learning to develop novel solutions that enhance Power, Performance, and Area (PPA) in our Systems on Chip (SoC) designs. Your work will directly contribute to better chip design, reduced design cycle times, and more efficient silicon creating ripple effects across multiple product lines including Pixel devices, Nest products, and beyond.

Minimum Qualifications

• Bachelor’s degree in Electrical Engineering, Computer Engineering, or a related technical field or equivalent practical experience.
• At least 5 years of hands-on experience in Physical Design, including working knowledge of design flows, constraints management, and physical verification.
• Proficient programming skills in Python or C++ able to write clean, efficient, and scalable code.
• Foundational experience applying machine learning techniques to solve real-world problems.

Preferred Qualifications

• 10+ years of experience in Synthesis and Physical Design, with deep understanding of design closure, timing analysis, floor-planning, place and route, and design optimization strategies.

• Proficiency in various machine learning paradigms, including but not limited to

  • Supervised and unsupervised learning (e.g., logistic regression, SVMs, clustering)

  • Deep learning (e.g., neural networks, CNNs, RNNs)

  • Reinforcement learning (e.g., Q-learning, policy gradient methods)

• Strong grasp of algorithms and data structures, especially those relevant to optimization and pattern recognition.
• Experience integrating ML workflows with EDA (Electronic Design Automation) tools and physical design flows.

Responsibilities

• Collaborate with world-class hardware and software engineers, ML researchers, and CAD teams to develop and deploy machine learning solutions that optimize silicon design across Power, Performance, and Area (PPA).
• Use data-driven methods to analyze and enhance key components of the SoC design process, including logic synthesis, floor-plan optimization, placement, routing, clocking, timing closure, and PDN (Power Delivery Network) analysis.
• Interface with teams across Alphabet, including Google DeepMind, to bring cutting-edge AI research into silicon design workflows and develop highly efficient design methodologies.
• Drive the automation of complex design tasks using machine learning models that learn from past designs and predict optimal paths to closure.
• Work closely with internal ML teams to build scalable solutions and apply them across multiple product cycles to reduce time-to-market and improve silicon quality.
• Act as a key contributor to the development of innovative methodologies, sharing insights across teams and helping build a robust ML design ecosystem within Google.

What We’re Looking For

You are a curious and driven engineer who thrives in a multidisciplinary environment where hardware, software, and AI converge. You not only understand the technical depths of physical design but are also passionate about applying machine learning to unlock new efficiencies. You love solving hard problems, collaborating with exceptional teams, and building systems that scale across multiple product generations.

Why Google

At Google, we are not just creating products, we're building helpful, intelligent, and beautiful experiences that transform the way people live and work. Our hardware engineering teams are central to this mission, enabling innovative designs that power everything from mobile phones and smart speakers to wearables and smart home devices. You’ll have the opportunity to work at the intersection of AI, machine learning, and custom silicon, bringing ideas from concept to impact.

We are also proud to be an equal opportunity employer that values diversity and inclusion. We support a work environment where everyone regardless of background is empowered to do their best work.