Machine Learning Engineer Jobs in Palo Alto, CA
Machine Learning Engineer jobs in Palo Alto are in strong demand, concentrated in the Stanford Research Park, the University Avenue corridor, and the California Avenue district across AI research, enterprise software, and life sciences. Employers hiring right now include JPMorganChase, Tesla, and Rivian. Scan the live roles below and apply to whichever ones fit.
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Showing 5 of 1,026+ Machine Learning Engineer 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 1,026+ Machine Learning Engineer Jobs in Palo Alto
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Find JobsMachine Learning Engineer Job Market in Palo Alto
Who's Hiring
- JPMorganChase191
- Tesla117
- Rivian88
- GEICO73
- WindBorne Systems59
Top Industries Hiring
- Technology & Software264
- Manufacturing73
- Insurance59
- Biotechnology & Pharmaceuticals44
- Social Media29
Machine Learning Engineer Jobs in Palo Alto: Frequently Asked Questions
How do I get a machine learning engineer job in Palo Alto?
Focus your search on the AI research labs, enterprise software companies, and life sciences firms clustered in Stanford Research Park and along the California Avenue corridor. Candidates who demonstrate hands-on experience with large-scale model training, MLOps pipelines, or domain-specific applications such as drug discovery or autonomous systems stand out locally. A strong GitHub portfolio and familiarity with the deep learning frameworks common in Palo Alto's research-driven culture give applicants a clear edge.
Which companies hire machine learning engineers in Palo Alto?
Employers hiring machine learning engineers in Palo Alto right now include JPMorganChase, Tesla, and Rivian, based on current listings on Migrate Mate as of September 2026. Palo Alto's hiring mix leans heavily toward research-oriented tech companies, AI-native startups, and established enterprises with dedicated ML divisions.
Are there remote machine learning engineer jobs in Palo Alto?
Yes, though it depends on the role. Machine learning engineering is relatively remote-friendly when work centers on model development, experimentation, or data pipeline work, but on-site is common for roles requiring access to proprietary hardware or close collaboration with research teams. About 38% of machine learning engineer openings tied to Palo Alto are remote or hybrid as of September 2026. Roles at AI-native startups in Palo Alto tend to offer the most hybrid flexibility.
How can I get a machine learning engineer job in Palo Alto with little or no experience?
The most realistic entry path in Palo Alto is targeting ML research assistant, data scientist, or junior ML engineer roles at Stanford-affiliated spin-offs and early-stage AI startups in the University Avenue and California Avenue areas. These employers are more willing to invest in candidates with strong academic projects, Kaggle competition results, or published research than large incumbents requiring years of production experience. Contract and internship-to-hire roles at Palo Alto life sciences and autonomous vehicle companies also provide structured entry points.
Which industries hire the most machine learning engineers in Palo Alto?
Most machine learning engineer openings in Palo Alto sit in Technology & Software, Manufacturing, and Insurance, per current listings on Migrate Mate as of September 2026. Palo Alto's proximity to Stanford and its concentration of deep-tech venture capital draw research-intensive companies in these sectors, sustaining consistent ML hiring throughout the year.
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