Machine Learning Intern Jobs in Palo Alto, CA
Machine Learning Intern jobs in Palo Alto are in strong demand, concentrated in the Stanford Research Park, Downtown Palo Alto, and the California Avenue corridor across AI research, enterprise software, and biotech. Employers posting roles right now include JPMorganChase, Tesla, and GEICO. Scan the live roles below and apply to whichever ones fit.
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Showing 5 of 815+ Machine Learning Intern 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 815+ Machine Learning Intern Jobs in Palo Alto
Find roles in Palo Alto that match your experience and apply in just a few clicks.
Find Machine Learning Intern JobsMachine Learning Intern Job Market in Palo Alto
Who's Hiring
- JPMorganChase172
- Tesla110
- GEICO78
- WindBorne Systems63
- Nubank47
Top Industries Hiring
- Technology & Software125
- Manufacturing78
- Insurance63
- Biotechnology & Pharmaceuticals47
- Social Media31
Machine Learning Intern Jobs in Palo Alto: Frequently Asked Questions
How do I get a machine learning intern job in Palo Alto?
The strongest path in Palo Alto is targeting AI research labs, enterprise software companies, and early-stage startups clustered in Stanford Research Park and the Downtown corridor. Candidates who can demonstrate hands-on Python or PyTorch experience and point to a personal project or course project repository move faster through screening. Many Palo Alto teams also recruit directly through Stanford and university research connections, so engaging faculty or lab contacts gives local candidates a real edge.
Which companies hire machine learning interns in Palo Alto?
Palo Alto machine learning intern 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 market skews toward technology companies, AI-focused research organizations, and well-funded startups that maintain engineering offices in Palo Alto rather than larger corporate campuses elsewhere in the Bay Area.
Are there remote machine learning intern jobs in Palo Alto?
Yes, though it varies by role type: machine learning intern positions involving model training, data pipelines, and research analysis are more remote-compatible than those requiring lab hardware or on-site infrastructure. About 30% of machine learning intern openings tied to Palo Alto are remote or hybrid as of September 2026, reflecting broader tech flexibility in the area. Research and data-heavy subfields tend to offer the most remote availability locally.
How can I get a machine learning intern job in Palo Alto with little or no experience?
The most realistic entry path is applying to structured internship programs at Palo Alto's mid-size AI and software companies, which tend to have more defined onboarding than early-stage startups. Building a small public project using open datasets and documenting it clearly is the single most effective way to stand out without a prior internship. Research associate or data annotation roles at local AI labs also serve as a practical entry point that often converts to ML intern positions.
Which industries hire the most machine learning interns in Palo Alto?
The sectors hiring the most machine learning interns in Palo Alto are Technology & Software, Manufacturing, and Insurance, based on current listings on Migrate Mate as of September 2026. Palo Alto's proximity to Stanford and its dense concentration of AI-focused companies and venture-backed startups means those sectors consistently generate the highest intern demand in the city.
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