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Machine Learning Winter Internship Jobs in California

Escalon

Machine Learning Engineer

Santa Monica, CA

$100K - $120K/yr

The Role Our client is seeking a Machine Learning Engineer to help design and implement intelligent ... learning roles through internships, academic labs, or early career positions. * Strong ...

New

Capital One

Lead Machine Learning Engineer

San Jose, CA · On-site

$120K - $158K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of ... Internship experience does not apply) * At least 4 years of experience programming with Python ...

Capital One

Lead Machine Learning Engineer

San Francisco, CA · On-site +1

$120K - $159K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of ... Internship experience does not apply) * At least 4 years of experience programming with Python ...

Capital One

Lead Machine Learning Engineer

San Jose, CA · On-site +1

$120K - $158K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of ... Internship experience does not apply) * At least 4 years of experience programming with Python ...

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All JobsMachine Learning Winter JobsMachine Learning Winter Internship JobsMachine Learning Winter Internship Jobs in California

Machine Learning Winter Internship information

What types of projects and tasks can I expect to work on during a machine learning winter internship?

During a Machine Learning Winter Internship, you can expect to work on hands-on projects such as data preprocessing, building and evaluating machine learning models, and assisting with research experiments. Interns often contribute to real-world applications, such as developing predictive analytics tools, optimizing algorithms, or supporting deployment efforts. Collaboration is common, as you'll work closely with data scientists, engineers, and sometimes product teams, giving you exposure to the full machine learning workflow. This environment provides an excellent opportunity to apply theoretical knowledge, gain practical experience, and build a professional network in the field.

What is a machine learning winter internship?

A Machine Learning Winter Internship is a short-term, practical training program typically offered during the winter months for students or early-career professionals interested in machine learning. Interns work on real-world projects involving data analysis, model development, and algorithm implementation under the guidance of experienced mentors. The internship provides hands-on experience with tools and techniques used in the field, helping participants build technical skills and gain industry exposure. These positions are often offered by tech companies, research labs, or startups and can be either remote or onsite. Successful completion of a machine learning internship can enhance a candidate's resume and open up further career opportunities in artificial intelligence and data science.

What are the key skills and qualifications needed to thrive as a machine learning winter intern, and why are they important?

To thrive as a Machine Learning Winter Intern, you generally need a solid foundation in mathematics, programming (especially Python), and a basic understanding of machine learning concepts, often acquired through coursework or relevant projects. Familiarity with tools such as TensorFlow, PyTorch, and data analysis libraries like Pandas and NumPy is typically required. Strong problem-solving abilities, collaboration, and curiosity help interns stand out in team-based, fast-paced environments. These skills are crucial for effectively contributing to real-world projects and quickly learning from experienced professionals during the internship.

What is the difference between Machine Learning Winter Internship vs Data Science Winter Internship?

AspectMachine Learning Winter InternshipData Science Winter Internship
Required CredentialsUndergraduate or graduate in CS, AI, or related fields; some knowledge of ML frameworksUndergraduate or graduate in Statistics, Math, CS; familiarity with data analysis tools
Work EnvironmentResearch labs, tech companies, startups focusing on ML modelsData analysis, visualization, and interpretation in various industries
Employer & Industry UsageTech firms, AI startups, research institutionsTech companies, finance, healthcare, consulting

While both internships involve working with data, the Machine Learning Winter Internship emphasizes developing and applying ML algorithms, whereas the Data Science Winter Internship focuses on analyzing data, creating reports, and deriving insights. Candidates should choose based on their specific skills and career goals in AI or data analysis.

What are popular job titles related to Machine Learning Winter Internship jobs in California? For Machine Learning Winter Internship jobs in California, the most frequently searched job titles are:
What job categories do people searching Machine Learning Winter Internship jobs in California look for? The top searched job categories for Machine Learning Winter Internship jobs in California are:
What cities in California are hiring for Machine Learning Winter Internship jobs? Cities in California with the most Machine Learning Winter Internship job openings:
Machine Learning Winter Internship jobs near you
Infographic showing various Machine Learning Winter Internship job openings in California as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Mid-Level Machine Learning Engineer

TetraMem - Accelerate The World

San Jose, CA • On-site

Full-time

Re-posted 22 days ago

Job description

Job Summary:
TetraMem is a company focused on accelerating the world through innovative technology, and they are seeking a Mid-Level Machine Learning Engineer to develop and optimize machine learning models for edge AI applications. The role involves collaborating with hardware and software teams, providing mentorship, and researching state-of-the-art ML techniques to enhance model efficiency.
Responsibilities:
• Develop, optimize, and deploy lightweight machine learning models for edge AI applications, particularly for audio processing.
• Implement and optimize ML models on embedded platforms, including FPGA and custom ASIC solutions.
• Work closely with hardware and software teams to integrate ML models into production systems.
• Research and implement state-of-the-art ML techniques to enhance model efficiency, latency, and power consumption for embedded AI applications.
• Improve inference efficiency and model compression techniques, including quantization, pruning, and knowledge distillation.
• Collaborate with cross-functional teams to drive innovation and contribute to the overall system architecture.
• Provide technical leadership and mentorship to junior engineers.
• Publish research findings, present at conferences, and contribute to open-source projects when applicable.
Qualifications:
Required:
• 5+ years of experience or PhD in Computer Science, Electrical Engineering, or related fields.
• Strong experience in machine learning, with a focus on edge AI and lightweight model deployment.
• Expertise in ML frameworks such as PyTorch, TensorFlow, JAX.
• Proficiency in programming languages such as C/C++, Python, and experience with ML model optimization.
• Ability to work independently and collaboratively in a fast-paced startup environment.
• Ability to provide mentorship, technical guidance, and career development support to junior engineers and interns.
Preferred:
• Understanding of ML compiler and runtime design.
• Experience working with tools such as Optimum, ONNX, TensorRT, TFLite/LiteRT, ncnn, or CoreML.
• Familiarity with hardware acceleration techniques.
• Experience in embedded system development.
Company:
TetraMem is developing cutting-edge analog computing solutions for AI applications, offering exceptional performance with ultra-low power consumption. Founded in 2018, the company is headquartered in Newark, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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