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Internship Full Stack Machine Learning Engineer Jobs in California

Nace AI

Machine Learning Engineer

Palo Alto, CA · On-site

As a Machine Learning Engineer, you will play a central role in translating cutting-edge machine ... Familiarity with data processing stacks such as Spark and Airflow. * Experience with multi-node GPU ...

PhysicsX

Machine Learning Engineer

San Francisco, CA · On-site

We are building an AI-driven simulation software stack for engineering and manufacturing across ... Enhanced parental leave - 3 months full pay paternity and 6 months full pay maternity leave, to ...

TEKsystems

Machine Learning Engineer

San Diego, CA · Hybrid

$70 - $95/hr

We are seeking an MLOps Engineer to build, deploy, and optimize machine learning infrastructure ... As an industry leader in Full-Stack Technology Services, Talent Services, and real-world ...

Walmart

Staff, Machine Learning Engineer

Sunnyvale, CA · On-site

$130K - $260K/yr

We are looking for a strong Staff Machine Learning Engineer who has the passion to develop AI ... Design, Develop and deploy Full stack based applications. * Develop and deploy production-grade ...

Motorola

Machine Learning Engineer

Los Angeles, CA · On-site

... stack. * Collaborate with cross-functional teams to define machine learning use cases and evaluate ... D. in Electrical Engineering, Computer Science, or a related field. * Minimum of 3 years of ...

Intel

Machine Learning Engineer

Folsom, CA

Hands-on experience implementing and scaling the full **post-training pipeline** for language ... internship experiences and or schoolwork/classes/research. Benefits at Intel Our total rewards ...

Intel

Machine Learning Engineer

Santa Clara, CA · On-site

Hands-on experience implementing and scaling the full **post-training pipeline** for language ... internship experiences and or schoolwork/classes/research. Benefits at Intel Our total rewards ...

Poesis

Machine Learning Engineer

San Francisco, CA · On-site

$200K - $280K/yr

You'll work across the full machine learning lifecycle, from experimentation and model and agent ... Learning Engineer, or related role * Prior experience at a frontier AI lab, agentic startup ...

Adobe, Inc.

Machine Learning Engineer

San Jose, CA · On-site

The Opportunity Adobe is looking for a Machine Learning Engineer who will apply AI and machine ... Experience with the full ML lifecycle: feature engineering, model training, evaluation, deployment ...

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Internship Full Stack Machine Learning Engineer information

What are the key skills and qualifications needed to thrive as an Internship Full Stack Machine Learning Engineer, and why are they important?

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 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 types of projects and responsibilities can I expect as an Internship Full Stack Machine Learning Engineer?

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 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 California? The most popular types of Full Stack Machine Learning Engineer jobs in California are:
What are popular job titles related to Internship Full Stack Machine Learning Engineer jobs in California? For Internship Full Stack Machine Learning Engineer jobs in California, the most frequently searched job titles are:
What job categories do people searching Internship Full Stack Machine Learning Engineer jobs in California look for? The top searched job categories for Internship Full Stack Machine Learning Engineer jobs in California are:
What cities in California are hiring for Internship Full Stack Machine Learning Engineer jobs? Cities in California with the most Internship Full Stack Machine Learning Engineer job openings:
Internship Full Stack Machine Learning Engineer jobs near you

Machine Learning Engineer

Nace AI

Palo Alto, CA • On-site

Full-time

Re-posted 20 days ago

Job description

Role Overview:
As a Machine Learning Engineer, you will play a central role in translating cutting-edge machine learning research into scalable, production-ready solutions. You will collaborate closely with cross-functional teams to identify opportunities where ML can drive product value, architect robust model-centric systems, and ensure their seamless integration into real-world applications. The role requires a strong balance between theoretical understanding and engineering execution, with a focus on building reliable, maintainable, and high-impact AI-driven features that align with Nace.AI's strategic objectives.
Key Responsibilities:
  • Design, build, and maintain end-to-end ML systems, including synthetic data pipelines, model training, debugging, and performance evaluation.
  • Fine-tune large language models (LLMs) and implement meta-learning methods to enhance model generalization and efficiency.
  • Improve existing Nace.AI models by incorporating advancements from recent ML research.

Qualifications:
  • Hands-on experience training and fine-tuning large language models (LLMs) and vision-language models (VLMs), including practical work with pre-training, instruction tuning, and alignment techniques (GRPO,RLHF/DPO/PPO).
  • Hands-on Experience with Deep Learning Models, especially Transformers.
  • Ability to translate cutting-edge research from papers into clean, production-ready code (Paper to Code).
  • Proven experience scaling inference infrastructure for LLMs/VLMs, including expertise in model serving frameworks like vLLM, TGI.
  • Proficient in Python with a strong track record of building substantial projects.
  • Solid foundation in computer science fundamentals (data structures, algorithms, design patterns).
  • BS degree in CS or related technical field.
  • Solid Experience with ML frameworks and libraries (PyTorch, TensorFlow).
  • Self-starter comfortable working in a fast-paced, dynamic environment.

Preferred Qualifications:
  • MS/PhD in CS or related technical field.
  • Familiarity with data processing stacks such as Spark and Airflow.
  • Experience with multi-node GPU training.
  • Contributor to open-source ML projects.
  • Deep knowledge in Linear Programming.
  • Experience with advanced NLP and Multimodal post-training experience (e.g., model distillation, quantization, deployment optimization).
  • Experienced in inference time optimization, deep understanding of LLM serving optimizations for LLMs/VLMs.
  • Hands on experience with quantization techniques (AWQ, GPTQ, FP8/GGUF).

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