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Internship Full Stack Machine Learning Engineer Jobs in Santa Clara, CA

Abaka AI

Machine Learning Engineer

Mountain View, CA · On-site

$175K - $275K/yr

Whether teams need raw data, curated datasets, or full-cycle data engineering, Abaka AI provides ... About the Role We're hiring our first Machine Learning Engineer in the United States, a ...

NR Consulting

Machine Learning Engineer

Fremont, CA · On-site

NR Consulting is a company focused on innovative technology solutions, and they are seeking a Machine Learning Engineer to develop and deploy lightweight machine learning models for edge AI ...

Abaka AI

Machine Learning Engineer

Mountain View, CA · On-site

$175K - $275K/yr

Whether teams need raw data, curated datasets, or full-cycle data engineering, Abaka AI provides ... About the Role We're hiring our first Machine Learning Engineer in the United States, a ...

Skild AI

Machine Learning Engineer

San Mateo, CA · On-site

Position Overview We are looking for a Machine Learning Engineer to be responsible for designing and implementing cutting-edge reinforcement learning algorithms, conducting experiments, and ...

Adobe, Inc.

Machine Learning Engineer

San Jose, CA · On-site

Position: 2026 Machine Learning Engineer Req ID: Pending Location: San Jose Our Company Changing the world through digital experiences is what Adobe's all about. We give everyone-from emerging ...

Adobe

Machine Learning Engineer

San Jose, CA · On-site

$150 - $200/hr

Position: 2026 Machine Learning Engineer Req ID: Pending Location: San Jose Our Company Changing the world through digital experiences is what Adobe's all about. We give everyone-from emerging ...

Aven

Machine Learning Engineer

Campbell, CA · On-site

We are looking for Machine Learning Engineers who have built product models from idea to delivery ... Tech Stack: * AWS * PostgreSQL * Typescript * Python * VueJS (web) * Swift (iOS) * Kotlin/Java ...

Adobe

Machine Learning Engineer

San Jose, CA · On-site

Position: 2026 Machine Learning Engineer Req ID: Pending Location: San Jose Our Company Changing the world through digital experiences is what Adobe's all about. We give everyone-from emerging ...

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All JobsFull Stack Machine Learning Engineer JobsInternship Full Stack Machine Learning Engineer JobsInternship Full Stack Machine Learning Engineer Jobs in Santa Clara, CA

Internship Full Stack Machine Learning Engineer information

See Santa Clara, CA salary details

$52.3K

$158.3K

$223.7K

How much do internship full stack machine learning engineer jobs pay per year?

As of Sep 9, 2026, the average yearly pay for internship full stack machine learning engineer in Santa Clara, CA is $158,280.00, according to ZipRecruiter salary data. Most workers in this role earn between $130,400.00 and $185,600.00 per year, depending on experience, location, and employer.

What is an internship full stack machine learning engineer?

An Internship Full Stack Machine Learning Engineer is a student or early-career professional who supports both the development of machine learning models and the integration of these models into full-stack applications. This role typically involves working on data preprocessing, building and training machine learning algorithms, and deploying these models within web or mobile applications. Interns in this field gain experience in both backend and frontend technologies, as well as in machine learning frameworks and tools. The position is ideal for those seeking hands-on experience in applying AI solutions within real-world products.

What do internship full stack machine learning engineers do?

As an Internship Full Stack Machine Learning Engineer, you can expect to work on end-to-end machine learning projects that involve both model development and integration into web or cloud applications. This may include tasks like cleaning and preparing datasets, building and testing machine learning models, developing APIs to serve predictions, and collaborating with front-end developers to deliver user-facing features. Interns often work closely with data scientists, software engineers, and product managers, gaining exposure to the full development lifecycle. These experiences help build both technical and teamwork skills, laying a strong foundation for a future career in the field.

What skills and qualifications are needed to thrive as an internship full stack machine learning engineer?

To succeed as an Internship Full Stack Machine Learning Engineer, you need a solid understanding of programming (Python, JavaScript), basic machine learning concepts, and foundational knowledge in computer science or a related field. Familiarity with frameworks like TensorFlow or PyTorch, web development tools (React, Node.js), and version control systems like Git is typically expected. Strong problem-solving abilities, collaboration skills, and a willingness to learn set exceptional interns apart. These skills enable interns to contribute effectively to both model development and deployment, bridging the gap between data science and software engineering in real-world applications.

What is the difference between Internship Full Stack Machine Learning Engineer vs Software Developer Intern?

AspectInternship Full Stack Machine Learning EngineerSoftware Developer Intern
Required SkillsKnowledge of machine learning, programming (Python, JavaScript), full stack development, data handlingProficiency in programming languages (Java, Python, JavaScript), software development, basic algorithms
Work EnvironmentCollaborates on ML models, data pipelines, backend and frontend developmentFocuses on application development, coding, debugging, and testing
Industry UsageUsed in AI-driven companies, tech startups, data science teamsCommon in software firms, app development companies, tech startups

The Internship Full Stack Machine Learning Engineer role emphasizes working with machine learning models and data-driven applications, combining full stack development skills with AI expertise. In contrast, a Software Developer Intern focuses more on traditional software development tasks like coding and debugging. Both roles are valuable entry points in tech, but they target different skill sets and project types.

What are the most commonly searched types of Full Stack Machine Learning Engineer jobs in Santa Clara, CA?

The most popular types of Full Stack Machine Learning Engineer jobs in Santa Clara, CA are:

What cities near Santa Clara, CA are hiring for Internship Full Stack Machine Learning Engineer jobs?

Cities near Santa Clara, CA with the most Internship Full Stack Machine Learning Engineer job openings:

Internship Full Stack Machine Learning Engineer jobs near you

Senior/Staff Machine Learning Engineer

Redwood City, CA • On-site

Dexterity
Software Development • 51 - 200 employees
Learn more about Dexterityexternal

$127K - $175K/yr

Full-time

Re-posted 9 days ago

Job description

About Dexterity
At Dexterity, we believe robots can positively transform the world. Our breakthrough technology frees people to do the creative, inspiring, problem-solving jobs that humans do best by enabling robots to handle repetitive and physically difficult work.

We're starting with warehouse automation, where the need for smarter, more resilient supply chains impacts millions of lives and businesses worldwide. Dexterity's full-stack robotics systems pick, move, pack, and collaborate with human-like skill, awareness, and learning capabilities. Our systems are software-driven and hardware-agnostic and have already picked 100+ million goods in production. And did we mention we're customer-obsessed? Every decision, large and small, is driven by one question - how can we empower our customers with robots to do more than they thought was possible?

Dexterity is one of the fastest-growing companies in robotics, backed by world-class investors such as Kleiner Perkins, Lightspeed Venture Partners, and Obvious Ventures. We're a diverse and multidisciplinary team with a culture built on passion, trust, and dedication. Come join Dexterity and help make intelligent robots a reality!

About the Role
As a Senior/Staff Machine Learning Engineer, you will be working on a myriad of challenges related to robot task and action planning. You will leverage techniques from machine learning to solve hard sequential decision problems that require reasoning about the physical world and its dynamics. You will also stay abreast of the latest progress in imitation learning, reinforcement learning, and other related fields in order to further develop Dexterity's technology foundations in Physical AI. Additionally, you will be responsible for updating and scaling our current ML pipelines to cover more scenarios and improve accuracy.

Dexterity's robotic solutions integrate data from a multitude of sensors, including RGB cameras, depth sensors, force-torque sensors, encoders, system telemetry and human input. To better inform the planning algorithms, you may work on sensor fusion and state estimation techniques to leverage this multimodal sensory data.

You will also work closely with the data platform, physics simulation, and robot operations teams to develop effective and efficient ways to improve the system's internal world model.

Dexterity has an expanding set of algorithmic challenges as we deploy new robotic applications, including areas such as:

- Improving packing algorithms to build taller, denser, more stable structures with a wider variety of objects.
- Solving the logistics task of moving and sorting inventory throughout a warehouse.
- Building models that understand physics and geometry for both short- and long-horizon tasks.

In addition to curating datasets and developing/improving machine learning models, you will be responsible for building data flywheels. Ideally, you will bring data-driven productization experience and help the team broadly in qualifying, deploying and updating models.
Responsibilities
  • Design and implement machine learning solutions across Dexterity's robotics stack, including but not limited to perception, decision-making, action scoring, and predictive modeling
  • Own the full ML development cycle for these solutions: data curation, labeling, training, evaluation, deployment, and iteration
  • Build performant training and inference pipelines using PyTorch, with production-readiness and scalability in mind
  • Collaborate closely with robotics, data platform, and simulation teams to integrate ML into real-time, latency-sensitive robotic systems
  • Use profiling, monitoring, and experiments to optimize model performance and reliability
  • Ensure reproducibility, traceability, and modularity across training and serving pipelines
  • Maintain clean, production-quality code in Python (and C++ where required)
  • Help establish best practices for model versioning, dataset management, and ML operations
Required Skills
  • Degree in Computer Science, Electrical Engineering, or Mathematics 5+ years of industry experience applying machine learning to real-world, production systemsStrong Python skills and deep experience with PyTorch
  • Ability to work fluently across ML tasks, e.g., classification, regression, ranking, segmentation, and structured prediction
  • Strong engineering background with experience profiling, debugging, and optimizing model and pipeline performance
  • Proven ability to design and maintain reliable systems, from model training to field deployment
  • Experience with cloud-based infrastructure (AWS, GCP, Azure) and containerized environments (Docker)
  • Familiarity with Linux, Git, CI/CD) and software development best practices (unit/acceptance/integration testing, code reviews)
Nice to haves
  • Prior experience in robotics, autonomous systems, or real-time ML applications
  • Exposure to multimodal data (e.g., RGBD, force-torque, pose estimates, telemetry)
  • Experience deploying models using serving stacks like NVIDIA Triton, TorchServe, or custom low-latency frameworks
  • Background in computer vision, geometric learning, or time-series modeling
  • Experience with Kubernetes, Ray or other distributed training and inference systems
  • Previous startup experience or experience in fast-paced, cross-disciplinary environments
$170,000 - $225,000 a year
Our Total Rewards philosophy is designed to recognize contributions toward meaningful innovation. Base pay is one component of a broader compensation package that may include equity grants, benefits, and other incentives, depending on role and eligibility.

For this position, the expected base salary range is $170,000 to $225,000 annually. Actual compensation will be determined based on skills, experience, education, and market factors, and may vary accordingly.

Final compensation decisions are made individually and take a number of factors into consideration. Eligible employees may be considered for equity awards as part of their overall compensation. Access to benefits and wellness resources is provided in accordance with company policies and may vary based on role and location.

Equal Opportunity Employer
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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