Software Engineering Manager Jobs in Palo Alto, CA
Software Engineering Manager jobs in Palo Alto are concentrated in the Stanford Research Park, California Avenue corridor, and downtown along University Avenue, with demand running deep across enterprise software, AI infrastructure, and semiconductor tooling. Tesla, JPMorganChase, and Rivian and Volkswagen Group Technologies are among those hiring right now. See the openings below and apply to the ones that match your experience.
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Elevate your career by leading high-impact engineering teams and shaping the future of machine learning platforms at JPMorgan Chase, driving innovative solutions that empower data scientists and ML engineers across the organization.
As a AI/ML Solutions Manager of Software Engineering at JPMorgan Chase in the Consumer and Community Banking Technology team, you will set strategic direction, oversee project delivery, and ensure alignment with business objectives for multiple engineering teams. Leveraging your leadership and technical expertise, you will guide the development of robust ML infrastructure and tools, foster a culture of technical excellence, and drive continuous improvement in platform capabilities. Your role will require exceptional collaboration and stakeholder management skills, as you empower teams, champion best practices, and represent the ML platform engineering function in cross-functional forums.
Job responsibilities
- Lead and manage engineering teams in the design, development, and maintenance of scalable machine learning platforms and infrastructure.
- Set strategic direction for ML platform initiatives, ensuring alignment with business goals and enterprise standards.
- Oversee the delivery of tools for model training, deployment, monitoring, and lifecycle management.
- Guide the integration of data engineering, feature management, and model serving capabilities into unified ML platform solutions.
- Ensure the implementation of secure, high-quality production code for platform services, APIs, and automation pipelines.
- Collaborate with data scientists, ML engineers, product teams, and business stakeholders to define requirements and deliver impactful platform features.
- Drive platform reliability, scalability, and performance through proactive monitoring, troubleshooting, and continuous improvement.
- Foster a culture of technical excellence, innovation, and continuous learning within the engineering team.
- Represent the ML platform engineering function in cross-functional forums and contribute to the community of practice.
- Leads team adoption of enterprise-authorized AI-assisted engineering practices and SDLC/TLM automation to improve delivery speed, quality, and operational outcomes, while setting expectations for human validation, secure handling of inputs/outputs, and consistent use of reusable patterns across teams. Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation and support capacity unlock initiatives.
Required qualifications, capabilities, and skills
- 5+ years of applied experience or formal training/certification in software engineering concepts, including coaching and mentoring.
- Proven experience building, deploying, and maintaining machine learning platforms or infrastructure.
- Proficiency in Python and familiarity with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
- Experience with data processing frameworks and tools (e.g., Spark, Pandas, SQL).
- Strong understanding of cloud-based ML platforms (e.g., AWS SageMaker, GCP AI Platform, Azure ML) or on-prem ML infrastructure.
- Knowledge of MLOps practices, including CI/CD for ML, model versioning, and monitoring.
- Experience developing APIs and platform services for ML workflows.
- Solid understanding of the software development life cycle, agile methodologies, and engineering best practices.
- Demonstrated ability to lead and mentor engineering teams, and collaborate with cross-functional stakeholders.
- Experience leading responsible adoption of enterprise-authorized AI-assisted development and delivery tools across engineering teams, including defining ways of working (review/validation expectations), measuring outcomes, and ensuring secure handling of data. Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, resiliency/security implications, and governance expectations; ability to coach engineers on compliant and effective usage.
Preferred qualifications, capabilities, and skills
- Experience with Databricks for scalable data engineering and ML platform integration.
- Experience with Snowflake for cloud-based data warehousing and analytics.
- Exposure to Snorkel AI for programmatic data labeling and training data management.
- Experience with containerization and orchestration tools (e.g., Docker, Kubernetes, Airflow).
- Familiarity with feature stores, model registries, and ML metadata management.
- Experience with infrastructure-as-code tools (e.g., Terraform, CloudFormation). Experience with RESTful APIs and microservices architectures.
This position is subject to Section 19 of the Federal Deposit Insurance Act. As such, an employment offer for this position is contingent on JPMorgan Chase's review of criminal conviction history, including pretrial diversions or program entries.
Chase is a leading financial services firm, helping nearly half of America's households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs.
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
Equal Opportunity Employer/Disability/Veterans
ABOUT THE TEAM
Our Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We're proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions – all while ranking first in customer satisfaction.
Elevate your career by leading high-impact engineering teams and shaping the future of machine learning platforms at JPMorgan Chase, driving innovative solutions that empower data scientists and ML engineers across the organization.
As a AI/ML Solutions Manager of Software Engineering at JPMorgan Chase in the Consumer and Community Banking Technology team, you will set strategic direction, oversee project delivery, and ensure alignment with business objectives for multiple engineering teams. Leveraging your leadership and technical expertise, you will guide the development of robust ML infrastructure and tools, foster a culture of technical excellence, and drive continuous improvement in platform capabilities. Your role will require exceptional collaboration and stakeholder management skills, as you empower teams, champion best practices, and represent the ML platform engineering function in cross-functional forums.
Job responsibilities
- Lead and manage engineering teams in the design, development, and maintenance of scalable machine learning platforms and infrastructure.
- Set strategic direction for ML platform initiatives, ensuring alignment with business goals and enterprise standards.
- Oversee the delivery of tools for model training, deployment, monitoring, and lifecycle management.
- Guide the integration of data engineering, feature management, and model serving capabilities into unified ML platform solutions.
- Ensure the implementation of secure, high-quality production code for platform services, APIs, and automation pipelines.
- Collaborate with data scientists, ML engineers, product teams, and business stakeholders to define requirements and deliver impactful platform features.
- Drive platform reliability, scalability, and performance through proactive monitoring, troubleshooting, and continuous improvement.
- Foster a culture of technical excellence, innovation, and continuous learning within the engineering team.
- Represent the ML platform engineering function in cross-functional forums and contribute to the community of practice.
- Leads team adoption of enterprise-authorized AI-assisted engineering practices and SDLC/TLM automation to improve delivery speed, quality, and operational outcomes, while setting expectations for human validation, secure handling of inputs/outputs, and consistent use of reusable patterns across teams. Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation and support capacity unlock initiatives.
Required qualifications, capabilities, and skills
- 5+ years of applied experience or formal training/certification in software engineering concepts, including coaching and mentoring.
- Proven experience building, deploying, and maintaining machine learning platforms or infrastructure.
- Proficiency in Python and familiarity with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
- Experience with data processing frameworks and tools (e.g., Spark, Pandas, SQL).
- Strong understanding of cloud-based ML platforms (e.g., AWS SageMaker, GCP AI Platform, Azure ML) or on-prem ML infrastructure.
- Knowledge of MLOps practices, including CI/CD for ML, model versioning, and monitoring.
- Experience developing APIs and platform services for ML workflows.
- Solid understanding of the software development life cycle, agile methodologies, and engineering best practices.
- Demonstrated ability to lead and mentor engineering teams, and collaborate with cross-functional stakeholders.
- Experience leading responsible adoption of enterprise-authorized AI-assisted development and delivery tools across engineering teams, including defining ways of working (review/validation expectations), measuring outcomes, and ensuring secure handling of data. Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, resiliency/security implications, and governance expectations; ability to coach engineers on compliant and effective usage.
Preferred qualifications, capabilities, and skills
- Experience with Databricks for scalable data engineering and ML platform integration.
- Experience with Snowflake for cloud-based data warehousing and analytics.
- Exposure to Snorkel AI for programmatic data labeling and training data management.
- Experience with containerization and orchestration tools (e.g., Docker, Kubernetes, Airflow).
- Familiarity with feature stores, model registries, and ML metadata management.
- Experience with infrastructure-as-code tools (e.g., Terraform, CloudFormation). Experience with RESTful APIs and microservices architectures.
This position is subject to Section 19 of the Federal Deposit Insurance Act. As such, an employment offer for this position is contingent on JPMorgan Chase's review of criminal conviction history, including pretrial diversions or program entries.
Chase is a leading financial services firm, helping nearly half of America's households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs.
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
Equal Opportunity Employer/Disability/Veterans
ABOUT THE TEAM
Our Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We're proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions – all while ranking first in customer satisfaction.
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Find JobsSoftware Engineering Manager Job Market in Palo Alto
Who's Hiring
- Tesla215
- JPMorganChase61
- Rivian and Volkswagen Group Technologies61
- GEICO31
- Salesforce15
Top Industries Hiring
- Manufacturing92
- Fintech31
- Insurance31
- Banking & Financial Services31
- Investment & Asset Management31
Software Engineering Manager Jobs in Palo Alto: Frequently Asked Questions
How do I get a software engineering manager job in Palo Alto?
The strongest path is targeting Palo Alto's dense cluster of enterprise software, developer tools, and AI platform companies, particularly those anchored in Stanford Research Park and the California Avenue tech corridor. Hiring managers here weight hands-on engineering depth heavily, so candidates who can speak credibly to system design decisions, not just team process, stand out. Prior experience at a Bay Area product company, even in a senior IC role, meaningfully shortens the interview cycle at most local employers.
Which companies hire software engineering managers in Palo Alto?
Employers hiring software engineering managers in Palo Alto right now include Tesla, JPMorganChase, and Rivian and Volkswagen Group Technologies, based on current listings on Migrate Mate as of August 2026. Palo Alto's employer mix skews toward established product companies and deep-tech firms rather than early-stage startups, so openings tend to carry well-defined scope and multi-team leadership expectations.
Are there remote software engineering manager jobs in Palo Alto?
Yes, though fully remote roles are less common for software engineering managers than for individual contributors, since the role typically involves real-time team coordination, performance conversations, and cross-functional stakeholder work that many Palo Alto employers prefer to keep partially on-site. About 0% of software engineering manager openings tied to Palo Alto are remote or hybrid as of August 2026. Hybrid arrangements with two to three anchor days are the most common format among established Palo Alto technology employers.
How can I get a software engineering manager job in Palo Alto with little or no experience?
The most realistic entry path is moving into a tech lead or staff engineer role at a Palo Alto-area product company first, then transitioning internally once you have direct reports or project ownership on your record. Companies in Stanford Research Park and along California Avenue frequently promote strong senior engineers into people manager roles. Gaining experience with performance cycles, roadmap planning, and cross-functional delivery in any capacity, even informally, makes that internal transition far more credible to local hiring committees.
Which industries hire the most software engineering managers in Palo Alto?
Most software engineering manager openings in Palo Alto sit in Manufacturing, Fintech, and Insurance, per current listings on Migrate Mate as of August 2026. Palo Alto's proximity to Stanford and its long history as a hub for instrumentation and semiconductor IP means the local employer base skews toward technically demanding product areas, which drives consistent demand for engineering managers who can bridge deep technical work and organizational execution.
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