Machine Learning Engineer Jobs in Mountain View, CA
Machine Learning Engineer jobs in Mountain View are in strong demand, concentrated in North Bayshore, the Castro Street corridor, and the Moffett Field tech cluster, across AI research, autonomous systems, and cloud infrastructure. Companies hiring right now include ServiceNow, Nuro, and Google. Find a role that fits below and apply directly.
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Showing 5 of 1,026+ Machine Learning Engineer jobs
Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 8 years of experience with software design and architecture.
- Experience with C, C++, machine learning, and embedded systems.
- Experience with machine learning algorithms.
- Experience with machine learning architecture.
- Experience with machine learning research.
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- Experience taking machine learning models from research prototype to production on mobile or embedded devices, and owning them after launch.
- Experience optimizing models for on-device accelerators through quantization, hardware-aware architecture design, or custom kernels.
- Experience building and training models in JAX/TensorFlow.
About the job
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
The Google Pixel team focuses on designing and delivering the world's most helpful mobile experience. The team works on shaping the future of Pixel devices and services through some of the most advanced designs, techniques, products, and experiences in consumer electronics. This includes bringing together the best of Google’s artificial intelligence, software, and hardware to build global smartphones and create transformative experiences for users across the world.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Design and implement the real-time on-device machine learning foundation that turns raw camera and sensor input into structured signals driving immediate application behavior.
- Drive the optimization of outlook models for NPU, GPU, and DSP execution, including quantization, hardware-aware architecture design, and custom kernel development, in partnership with the Tensor silicon and compiler teams.
- Own models end-to-end, from prototype through deployment on hundreds of millions of devices — training pipelines in JAX and TensorFlow, C++ integration into the camera pipeline, and long-term maintainability.
- Partner with product, UX, software, and hardware teams to define the requirements for next-generation interactive features, and translate roadmap goals into designs achievable within device constraints.
- Establish best practices for machine learning development, deployment, and evaluation. Define how model quality is measured; and contribute to the long-term technology roadmap.
Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 8 years of experience with software design and architecture.
- Experience with C, C++, machine learning, and embedded systems.
- Experience with machine learning algorithms.
- Experience with machine learning architecture.
- Experience with machine learning research.
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- Experience taking machine learning models from research prototype to production on mobile or embedded devices, and owning them after launch.
- Experience optimizing models for on-device accelerators through quantization, hardware-aware architecture design, or custom kernels.
- Experience building and training models in JAX/TensorFlow.
About the job
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
The Google Pixel team focuses on designing and delivering the world's most helpful mobile experience. The team works on shaping the future of Pixel devices and services through some of the most advanced designs, techniques, products, and experiences in consumer electronics. This includes bringing together the best of Google’s artificial intelligence, software, and hardware to build global smartphones and create transformative experiences for users across the world.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Design and implement the real-time on-device machine learning foundation that turns raw camera and sensor input into structured signals driving immediate application behavior.
- Drive the optimization of outlook models for NPU, GPU, and DSP execution, including quantization, hardware-aware architecture design, and custom kernel development, in partnership with the Tensor silicon and compiler teams.
- Own models end-to-end, from prototype through deployment on hundreds of millions of devices — training pipelines in JAX and TensorFlow, C++ integration into the camera pipeline, and long-term maintainability.
- Partner with product, UX, software, and hardware teams to define the requirements for next-generation interactive features, and translate roadmap goals into designs achievable within device constraints.
- Establish best practices for machine learning development, deployment, and evaluation. Define how model quality is measured; and contribute to the long-term technology roadmap.
See All 1,026+ Machine Learning Engineer Jobs in Mountain View
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Find JobsMachine Learning Engineer Job Market in Mountain View
Who's Hiring
- ServiceNow165
- Nuro127
- Google114
- General Motors114
- Coupang76
Top Industries Hiring
- Technology & Software203
- Electronics & Hardware127
- Automotive114
- Banking & Financial Services114
- Artificial Intelligence38
Machine Learning Engineer Jobs in Mountain View: Frequently Asked Questions
How do I get a machine learning engineer job in Mountain View?
Mountain View's machine learning market centers on AI platforms, autonomous vehicles, and developer tools, with the heaviest hiring from large tech campuses in North Bayshore and smaller deep-tech startups near Castro Street. Candidates who stand out here combine strong Python and ML framework skills with experience deploying models at scale. Contributing to open-source projects used by Mountain View employers and targeting roles at both established tech giants and Series B startups improves your odds significantly.
Which companies hire machine learning engineers in Mountain View?
Companies currently hiring machine learning engineers in Mountain View include ServiceNow, Nuro, and Google, per current listings on Migrate Mate as of September 2026. Mountain View's employer mix is weighted toward large-scale AI and platform companies alongside a steady pipeline of well-funded deep-tech startups in autonomous systems and developer tooling.
Are there remote machine learning engineer jobs in Mountain View?
Yes, though many Mountain View machine learning engineer roles favor on-site or hybrid work because of the collaborative research culture at major campuses here. About 45% of machine learning engineer openings tied to Mountain View are remote or hybrid as of September 2026, with the most flexible arrangements concentrated in MLOps, model evaluation, and data pipeline work rather than core research positions.
How can I get a machine learning engineer job in Mountain View with little or no experience?
The most realistic entry path in Mountain View is through associate or junior ML engineer roles at mid-size AI startups near Castro Street or Moffett Field, where teams are smaller and responsibilities expand quickly. Internships at Mountain View's large tech campuses also convert to full-time roles at a higher rate than most markets. Building a portfolio of end-to-end projects, contributing to open-source ML libraries, and targeting applied science or ML infrastructure support roles gives candidates without a graduate degree a concrete foothold locally.
Which industries hire the most machine learning engineers in Mountain View?
Mountain View machine learning engineer roles concentrate in Technology & Software, Electronics & Hardware, and Automotive, based on current listings on Migrate Mate as of September 2026. Mountain View's position as a global AI research hub means those sectors draw both product-focused and fundamental research talent, sustaining consistent demand across experience levels.
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