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, Rivian, and Tesla. Scan the live roles below and apply to whichever ones fit.
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Showing 5 of 708+ Machine Learning Intern jobs
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.
As an Applied Machine Learning Scientist within our dynamic team, you will lead the development of scalable, production-grade advanced ML solutions across natural language processing, speech recognition, recommendation systems, information retrieval, and agentic AI. You will play a key role in delivering Generative AI capabilities – designing and productionizing LLM-powered systems such as RAG (Retrieval Augmented Generation), tool/function-calling agents, and structured generation to automate complex workflows and improve customer experiences. You will collaborate with product, engineering, and control partners to translate ambiguous problems into measurable goals, deliver robust models, and operate them reliably in production. You bring strong deep learning and transformer-based modeling expertise, as well as hands-on experience in fine-tuning and evaluation. You must have a strong passion for machine learning, strong analytical thinking, a deep desire to learn, and high motivation. You must also invest independent time in learning, researching, and experimenting with new innovations, and contribute to a strong knowledge-sharing culture.
Job Responsibilities
Lead and deploy state-of-the-art advanced machine learning systems across NLP, speech recognition, recommendation systems, and information retrieval.
Design and build agentic AI systems for multi‑step workflows, including tool/function calling, multi‑agent orchestration, planning, grounding, and safety guardrails.
Use reinforcement learning (policy optimization, bandits, RLHF‑style approaches where appropriate) to improve personalization, dialog policies, and sequential decision‑making systems.
Fine-tune and adapt LLMs/SLMs using PEFT (LoRA, AdaLoRA, IA3), distillation, and quantization; optimize for quality, latency, cost, and production constraints.
Select and innovate on ML strategies for various banking problems.
Analyze and evaluate the ongoing performance of developed ML systems.
Collaborate with multiple partner teams, such as Business, Technology, Product Management, Design, Analytics, and Model Governance to deploy solutions into production.
Build domain understanding to identify high-impact opportunities, ensure responsible AI usage, and drive measurable outcomes (customer experience, automation, accuracy, and efficiency).
Implement privacy, safety, and security controls for GenAI systems, including PCI handling/redaction, policy checks, jailbreak resistance, and auditability.
Required qualifications, capabilities, and skills
MS with 7+ years, or PhD with 4+ years of hand-on industry experience in building and deploying machine learning systems (NLP/Information Retrieval/Recommendation System and/or GenAI) in production environment
Good understanding of the latest advancement of NLP concepts, such as the transformer architecture, knowledge distillation, transfer learning, and representation learning.
Applied GenAI experience with LLMs and the ability to fine‑tune and deploy SLMs for targeted use cases, familiarity with prompt design, grounded generation, and RAG.
Experience with scaling LLM systems (caching, batching, prompt/version governance, evaluation harnesses)
Strong foundation in machine learning, deep learning, and statistical modelling, including model evaluation and error analysis.
Solid understanding of Information Retrieval concepts (indexing, ranking, dense/sparse retrieval, re-ranking) and/or recommendation systems.
Ability to design experiments — establish strong baselines, choose meaningful metrics, and evaluate model performance rigorously
Scientific thinking with the ability to invent and to work both independently and in highly collaborative team environments
Proficiency in Python and common ML libraries (PyTorch/TensorFlow, Hugging Face, scikit-learn), and ability to write production-quality code.
Ability to collaborate in cross-functional environments with product, engineering, and control partners.
Solid written and spoken communication skills
Preferred qualifications, capabilities, and skills
5 years of hands-on experience with virtual assistant model development and optimization
Experience orchestrating multi‑agent teams with supervisor agents, debate/consensus mechanisms, and role‑specialized toolkits for complex enterprise tasks.
Building agent governance and eval suites: red‑teaming, adversarial tests, safety scorecards, regression suites for prompts/tools
Experience with RL/bandits, preference optimization, or human feedback loops for personalization.
Experience in regulated finance domains and working with risk/control processes.
Experience with MLOps/LLMOps: CI/CD for models, monitoring/alerting, model versioning, evaluation of pipelines, and rollback strategies.
Experience with A/B experimentation and data/metric-driven product development.
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 USChase 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
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.
As an Applied Machine Learning Scientist within our dynamic team, you will lead the development of scalable, production-grade advanced ML solutions across natural language processing, speech recognition, recommendation systems, information retrieval, and agentic AI. You will play a key role in delivering Generative AI capabilities – designing and productionizing LLM-powered systems such as RAG (Retrieval Augmented Generation), tool/function-calling agents, and structured generation to automate complex workflows and improve customer experiences. You will collaborate with product, engineering, and control partners to translate ambiguous problems into measurable goals, deliver robust models, and operate them reliably in production. You bring strong deep learning and transformer-based modeling expertise, as well as hands-on experience in fine-tuning and evaluation. You must have a strong passion for machine learning, strong analytical thinking, a deep desire to learn, and high motivation. You must also invest independent time in learning, researching, and experimenting with new innovations, and contribute to a strong knowledge-sharing culture.
Job Responsibilities
Lead and deploy state-of-the-art advanced machine learning systems across NLP, speech recognition, recommendation systems, and information retrieval.
Design and build agentic AI systems for multi‑step workflows, including tool/function calling, multi‑agent orchestration, planning, grounding, and safety guardrails.
Use reinforcement learning (policy optimization, bandits, RLHF‑style approaches where appropriate) to improve personalization, dialog policies, and sequential decision‑making systems.
Fine-tune and adapt LLMs/SLMs using PEFT (LoRA, AdaLoRA, IA3), distillation, and quantization; optimize for quality, latency, cost, and production constraints.
Select and innovate on ML strategies for various banking problems.
Analyze and evaluate the ongoing performance of developed ML systems.
Collaborate with multiple partner teams, such as Business, Technology, Product Management, Design, Analytics, and Model Governance to deploy solutions into production.
Build domain understanding to identify high-impact opportunities, ensure responsible AI usage, and drive measurable outcomes (customer experience, automation, accuracy, and efficiency).
Implement privacy, safety, and security controls for GenAI systems, including PCI handling/redaction, policy checks, jailbreak resistance, and auditability.
Required qualifications, capabilities, and skills
MS with 7+ years, or PhD with 4+ years of hand-on industry experience in building and deploying machine learning systems (NLP/Information Retrieval/Recommendation System and/or GenAI) in production environment
Good understanding of the latest advancement of NLP concepts, such as the transformer architecture, knowledge distillation, transfer learning, and representation learning.
Applied GenAI experience with LLMs and the ability to fine‑tune and deploy SLMs for targeted use cases, familiarity with prompt design, grounded generation, and RAG.
Experience with scaling LLM systems (caching, batching, prompt/version governance, evaluation harnesses)
Strong foundation in machine learning, deep learning, and statistical modelling, including model evaluation and error analysis.
Solid understanding of Information Retrieval concepts (indexing, ranking, dense/sparse retrieval, re-ranking) and/or recommendation systems.
Ability to design experiments — establish strong baselines, choose meaningful metrics, and evaluate model performance rigorously
Scientific thinking with the ability to invent and to work both independently and in highly collaborative team environments
Proficiency in Python and common ML libraries (PyTorch/TensorFlow, Hugging Face, scikit-learn), and ability to write production-quality code.
Ability to collaborate in cross-functional environments with product, engineering, and control partners.
Solid written and spoken communication skills
Preferred qualifications, capabilities, and skills
5 years of hands-on experience with virtual assistant model development and optimization
Experience orchestrating multi‑agent teams with supervisor agents, debate/consensus mechanisms, and role‑specialized toolkits for complex enterprise tasks.
Building agent governance and eval suites: red‑teaming, adversarial tests, safety scorecards, regression suites for prompts/tools
Experience with RL/bandits, preference optimization, or human feedback loops for personalization.
Experience in regulated finance domains and working with risk/control processes.
Experience with MLOps/LLMOps: CI/CD for models, monitoring/alerting, model versioning, evaluation of pipelines, and rollback strategies.
Experience with A/B experimentation and data/metric-driven product development.
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 USChase 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
See All 708+ Machine Learning Intern Jobs in Palo Alto
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Find Machine Learning Intern JobsMachine Learning Intern Job Market in Palo Alto
Who's Hiring
- JPMorganChase133
- Rivian118
- Tesla74
- Snap59
- GEICO44
Top Industries Hiring
- Technology & Software89
- Biotechnology & Pharmaceuticals44
- Insurance30
- Social Media30
- Manufacturing30
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, Rivian, and Tesla and others right now, based on current listings on Migrate Mate as of November 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 38% of machine learning intern openings tied to Palo Alto are remote or hybrid as of November 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, Biotechnology & Pharmaceuticals, and Insurance, based on current listings on Migrate Mate as of November 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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