Machine Learning Manager Jobs in Palo Alto, CA
Machine Learning Manager jobs in Palo Alto are in strong demand, concentrated along the University Avenue corridor, California Avenue, and the Stanford Research Park, across AI infrastructure, enterprise software, and life sciences. Employers hiring right now include JPMorganChase, Tesla, and GEICO. See the openings below and apply to the ones that match your experience.
Find Machine Learning Manager JobsOverview
Showing 5 of 830+ Machine Learning Manager jobs
We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Lead Software Engineer at JPMorganChase within AI/ML Data Platforms, you are an integral part of an agile team that works to build and operate scalable, reliable ML training systems and pipelines on AWS and other cloud platforms. You will productionize training workloads (often GPU-based), improve performance and cost efficiency, and enable repeatable, well-governed training across environments.
Job Responsibilities
Design, build, and maintain end-to-end ML training platform.
Run and optimize GPU training workloads (single-node and distributed), improving throughput, utilization and reproducibility.
Build and operate training infrastructure on Kubernetes (e.g., EKS and other manage Kubernetes platforms), including resource management and workload troubleshooting.
Enable Gen AI/LLM training and fine-tuning workflows (e.g., supervised fine-tuning), including evaluation harnesses, artifact/version governance, and scalable GPU execution patterns aligned to enterprise controls.
Implement observability for training systems: metrics, logs, dashboards, alerting, and operational runbooks.
Partner with data engineering and platform teams to define interfaces, standards, and guardrails (security, access, cost controls)
Improve developer experience for training: standardized containers, CI/CD, templates, documentation, and self-service workflow
Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
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.
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5+ years applied experience.
Demonstrated experience running ML training in cloud environments and debugging issues across infrastructure & code.
Strong Python skills with solid engineering practices (testing, code reviews, modular design, dependency management).
Experience building automation/CI for ML codebases (build, test, release, deployment/promotion workflows).
Hands on experience with deep learning training workflows and at least one major framework (eg., PyTorch or TensorFlow).
Understanding of training performance and stability: data loading bottlenecks, mixed precision, checkpointing, reproducibility, and evaluation methodology.
Experience with distributed training and related concepts (e.g., DDP/FSDP/DeepSpeed concepts, collective communication basics, scaling and bottleneck analysis).
Ability to profile and optimize training systems (CPU/GPU utilization, memory, I/O throughput, networking, scheduling).
Experience with Kubernetes fundamentals for running compute-intensive workloads and AWS (eg., EKS/ECR, S3, IAM, VPC/networking, Cloudwatch, EC2)
Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices.
Preferred qualifications, capabilities, and skills
Experience running training workloads across multiple cloud platforms and managing portability, performance, and governance across environments.
Familiarity with cloud-native networking/storage patterns for high-throughput training and artifact management.
Experience optimizing training input pipelines (sharding, prefetching, caching, format choices such as Parquet/WebDataset) and working with large datasets.
Familiarity with distributed compute frameworks (Spark, Ray, Dask) for feature/dataset generation.
Familiarity with workflow orchestration tools (Airflow-like systems, Argo Workflows-like patterns) and model registry concepts.
Experience optimizing training cost/performance (right-sizing, scheduling policies, interruptible capacity strategies where applicable budge guardrails, quota planning).
Strong observability practice for training systems: metrics/logs/traces, GPU telemetry, dashboards, and alert tuning.
FEDERAL DEPOSIT INSURANCE ACT: This position is subject to Section 19 of the Federal Deposit Insurance Act. As such, an employment offer for this position is contingent on JPMorganChase's review of criminal conviction history, including pretrial diversions or program entries.
ABOUT USWe 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.
JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans
ABOUT THE TEAM
Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we're setting our businesses, clients, customers and employees up for success.
We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Lead Software Engineer at JPMorganChase within AI/ML Data Platforms, you are an integral part of an agile team that works to build and operate scalable, reliable ML training systems and pipelines on AWS and other cloud platforms. You will productionize training workloads (often GPU-based), improve performance and cost efficiency, and enable repeatable, well-governed training across environments.
Job Responsibilities
Design, build, and maintain end-to-end ML training platform.
Run and optimize GPU training workloads (single-node and distributed), improving throughput, utilization and reproducibility.
Build and operate training infrastructure on Kubernetes (e.g., EKS and other manage Kubernetes platforms), including resource management and workload troubleshooting.
Enable Gen AI/LLM training and fine-tuning workflows (e.g., supervised fine-tuning), including evaluation harnesses, artifact/version governance, and scalable GPU execution patterns aligned to enterprise controls.
Implement observability for training systems: metrics, logs, dashboards, alerting, and operational runbooks.
Partner with data engineering and platform teams to define interfaces, standards, and guardrails (security, access, cost controls)
Improve developer experience for training: standardized containers, CI/CD, templates, documentation, and self-service workflow
Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
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.
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5+ years applied experience.
Demonstrated experience running ML training in cloud environments and debugging issues across infrastructure & code.
Strong Python skills with solid engineering practices (testing, code reviews, modular design, dependency management).
Experience building automation/CI for ML codebases (build, test, release, deployment/promotion workflows).
Hands on experience with deep learning training workflows and at least one major framework (eg., PyTorch or TensorFlow).
Understanding of training performance and stability: data loading bottlenecks, mixed precision, checkpointing, reproducibility, and evaluation methodology.
Experience with distributed training and related concepts (e.g., DDP/FSDP/DeepSpeed concepts, collective communication basics, scaling and bottleneck analysis).
Ability to profile and optimize training systems (CPU/GPU utilization, memory, I/O throughput, networking, scheduling).
Experience with Kubernetes fundamentals for running compute-intensive workloads and AWS (eg., EKS/ECR, S3, IAM, VPC/networking, Cloudwatch, EC2)
Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices.
Preferred qualifications, capabilities, and skills
Experience running training workloads across multiple cloud platforms and managing portability, performance, and governance across environments.
Familiarity with cloud-native networking/storage patterns for high-throughput training and artifact management.
Experience optimizing training input pipelines (sharding, prefetching, caching, format choices such as Parquet/WebDataset) and working with large datasets.
Familiarity with distributed compute frameworks (Spark, Ray, Dask) for feature/dataset generation.
Familiarity with workflow orchestration tools (Airflow-like systems, Argo Workflows-like patterns) and model registry concepts.
Experience optimizing training cost/performance (right-sizing, scheduling policies, interruptible capacity strategies where applicable budge guardrails, quota planning).
Strong observability practice for training systems: metrics/logs/traces, GPU telemetry, dashboards, and alert tuning.
FEDERAL DEPOSIT INSURANCE ACT: This position is subject to Section 19 of the Federal Deposit Insurance Act. As such, an employment offer for this position is contingent on JPMorganChase's review of criminal conviction history, including pretrial diversions or program entries.
ABOUT USWe 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.
JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans
ABOUT THE TEAM
Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we're setting our businesses, clients, customers and employees up for success.
See All 830+ Machine Learning Manager Jobs in Palo Alto
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Find Machine Learning Manager JobsMachine Learning Manager Job Market in Palo Alto
Who's Hiring
- JPMorganChase176
- Tesla112
- GEICO80
- WindBorne Systems64
- Nubank48
Top Industries Hiring
- Technology & Software128
- Manufacturing80
- Insurance64
- Biotechnology & Pharmaceuticals48
- Social Media32
Machine Learning Manager Jobs in Palo Alto: Frequently Asked Questions
How do I get a machine learning manager job in Palo Alto?
The strongest path into a machine learning manager role in Palo Alto is through the city's dense cluster of AI-focused startups, established tech companies near Stanford Research Park, and life sciences firms on Page Mill Road. Candidates who combine hands-on ML engineering experience with cross-functional leadership stand out. Publishing research, contributing to open-source projects, or presenting at Bay Area ML meetups builds local visibility with hiring managers who recruit from those networks.
Which companies hire machine learning managers in Palo Alto?
Palo Alto machine learning manager roles are posted by JPMorganChase, Tesla, and GEICO and others right now, based on current listings on Migrate Mate as of September 2026. The local employer mix ranges from well-funded AI startups near University Avenue to large enterprise tech companies and biotech firms with R&D operations anchored near the Stanford campus.
Are there remote machine learning manager jobs in Palo Alto?
Yes, though remote availability varies: ML managers who handle modeling strategy, team oversight, and cross-functional alignment tend to have more flexibility than those running on-site infrastructure or lab environments. About 30% of machine learning manager openings tied to Palo Alto are remote or hybrid as of September 2026, with hybrid arrangements most common among enterprise software and AI platform companies concentrated around California Avenue.
How can I get a machine learning manager job in Palo Alto with little or no experience?
The most realistic entry path is moving into a senior ML engineer or tech lead role at a Palo Alto startup first, then stepping into people management as the team scales. Early-stage AI companies near University Avenue and the Stanford Research Park regularly promote strong individual contributors into manager roles rather than hiring externally. Involvement in Stanford-adjacent research groups or Bay Area ML communities also gives local hiring managers a concrete signal of leadership potential.
Which industries hire the most machine learning managers in Palo Alto?
Palo Alto machine learning manager roles concentrate in Technology & Software, Manufacturing, and Insurance, based on current listings on Migrate Mate as of September 2026. The city's proximity to Stanford University and a high density of venture-backed AI companies drives sustained hiring across those sectors, making Palo Alto one of the most active local markets for this role in the country.
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