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Google Ic Design Jobs (NOW HIRING)

Sr. Lead Machine Learning Engineer (IC)

Mclean, VA · On-site +1

$103K - $136K/yr

Design, build, and/or deliver ML models and components that solve real-world business problems ... Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google ...

Sr. Lead Machine Learning Engineer (IC)

Mclean, VA · On-site +1

$103K - $136K/yr

Design, build, and/or deliver ML models and components that solve real-world business problems ... Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google ...

Support architecture governance processes, technical reviews, and design authority boards ... IC architecture methodologies * Cloud platforms (AWS, Azure Government, Google Cloud) * Hybrid ...

Support architecture governance processes, technical reviews, and design authority boards ... IC architecture methodologies * Cloud platforms (AWS, Azure Government, Google Cloud) * Hybrid ...

Support architecture governance processes, technical reviews, and design authority boards ... IC architecture methodologies * Cloud platforms (AWS, Azure Government, Google Cloud) * Hybrid ...

Support architecture governance processes, technical reviews, and design authority boards ... IC architecture methodologies * Cloud platforms (AWS, Azure Government, Google Cloud) * Hybrid ...

Showing results 41-60

Google Ic Design information

See salary details

$80.5K

$139.4K

$182.5K

How much do google ic design jobs pay per year?

As of Aug 11, 2026, the average yearly pay for google ic design in the United States is $139,368.00, according to ZipRecruiter salary data. Most workers in this role earn between $136,000.00 and $136,000.00 per year, depending on experience, location, and employer.

What are some common challenges faced by IC design engineers at Google, and how does the team typically address them?

IC Design Engineers at Google frequently encounter challenges such as managing complex design specifications, meeting tight project timelines, and ensuring seamless integration with other hardware and software teams. To address these, the team emphasizes cross-functional collaboration, regular design reviews, and access to advanced simulation and verification tools. Engineers are encouraged to share knowledge and leverage Google's robust internal resources, which helps in efficiently resolving technical roadblocks and fostering continuous learning.

What is the difference between Google Ic Design vs Google Hardware Design?

AspectGoogle Ic DesignGoogle Hardware Design
Primary FocusIntegrated Circuit (IC) design and developmentHardware product design including devices and components
Required SkillsVLSI, circuit design, verification, CAD toolsElectrical engineering, PCB design, hardware prototyping
Work EnvironmentDesign labs, CAD software, simulation toolsProduct labs, prototyping facilities, manufacturing
Industry UsageSemiconductor, electronics manufacturingConsumer electronics, hardware products

Google Ic Design professionals focus on designing integrated circuits and chips, while Google Hardware Design involves creating complete hardware products. Both roles require electrical engineering skills, but Ic Design is more specialized in circuit development, whereas Hardware Design covers broader product development processes.

What does a Google IC design engineer do?

A Google IC (Integrated Circuit) Design engineer is responsible for designing, developing, and testing microchips and integrated circuits used in Google's hardware products. This includes tasks such as schematic design, layout, verification, and collaborating with cross-functional teams to optimize performance and power consumption. These engineers play a crucial role in ensuring the reliability and efficiency of chips used in devices like smartphones, servers, and other Google hardware. The role often involves using specialized software tools for simulation and validation, as well as staying up-to-date with advancements in semiconductor technology.

What are the key skills and qualifications needed to thrive as a Google IC design engineer?

To thrive as a Google IC Design Engineer, you need a strong background in electrical engineering, digital and analog circuit design, and experience with semiconductor technologies, usually supported by a relevant degree. Proficiency in industry-standard EDA tools (like Cadence, Synopsys, or Mentor Graphics), hardware description languages (such as Verilog or VHDL), and familiarity with silicon verification processes are typically required. Strong problem-solving abilities, collaboration, and effective communication are essential soft skills for success in multidisciplinary teams. These skills and qualifications ensure innovative, reliable chip designs that meet performance benchmarks and integrate seamlessly into complex systems.
More about Google Ic Design jobs
Infographic showing various Google Ic Design job openings in the United States as of August 2026, with employment types broken down into 10% Internship, and 90% Full Time. Highlights an 70% In-person, 10% Hybrid, and 20% Remote job distribution, with an average salary of $139,368 per year, or $67 per hour.

Sr. Lead Machine Learning Engineer (IC)

Capital One

Mclean, VA • On-site, Remote

$103K - $136K/yr

Full-time

This job post has expired today. Applications are no longer accepted.


Capital One rating

7.7

Company rating: 7.7 out of 10

Based on 146 frontline employees who took The Breakroom Quiz

93rd of 171 rated banks


Job description

Sr. Lead Machine Learning Engineer (IC)

As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You’ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You’ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. 

What You’ll Do:

The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following:

  • Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams

  • Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation)

  • Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment

  • Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications

  • Retrain, maintain, and monitor models in production

  • Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale.

  • Construct optimized data pipelines to feed ML models

  • Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code

  • Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI

  • Use programming languages like Python, Scala, or Java

Basic Qualifications:

  • Bachelor’s Degree 

  • At least 8 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)

  • At least 4 years of experience programming with Python, Scala, or Java 

  • At least 3 years of experience building, scaling, and optimizing ML systems

  • At least 2 years of experience leading teams developing ML solutions 

Preferred Qualifications:

  • Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field

  • Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform 

  • 4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or XGboost

  • 3+ years of experience developing performant, resilient, and maintainable code

  • 3+ years of experience with data gathering and preparation for ML models

  • 3+ years of people management experience 

  • ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents

  • 3+ years of experience building production-ready data pipelines that feed ML models 

  • Ability to communicate complex technical concepts clearly to a variety of audiences 

  • Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion

Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.

The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.

McLean, VA: $229,900 - $262,400 for Sr. Lead Machine Learning Engineer


 

New York, NY: $250,800 - $286,200 for Sr. Lead Machine Learning Engineer


 

Richmond, VA: $209,000 - $238,500 for Sr. Lead Machine Learning Engineer


 


 


 


 


 


 


 


 

Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter.

This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.

Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.

This role is expected to accept applications for a minimum of 5 business days. No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City’s Fair Chance Act; Philadelphia’s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.

If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.

For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com

Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.

Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).


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