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Junior Machine Learning Jobs in California (NOW HIRING)

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Lead Machine Learning Engineer

Mountain View, CA · Hybrid

$170K - $190K/yr

We are looking for a Machine Learning Engineer to help build and evaluate the core intelligence ... Demonstrate proficiency in the technical mentorship of junior and mid-level engineers, driving the ...

FICO

Lead Machine Learning Engineer

San Diego, CA · On-site

$122K - $192K/yr

Mentor junior engineers and scientists. * Travel and work outside of normal business hours as ... machine learning; experience working with cloud-based and distributed architectures, large real ...

Adobe, Inc.

Sr. Machine Learning Engineer 4

San Jose, CA · On-site

$122K - $168K/yr

Mentor junior engineers and help grow the team's technical depth. What You Need to Succeed Required Qualifications * Master's or Ph.D. in Computer Science, Machine Learning, or a related technical ...

EliseAI

Junior Research Scientist

San Francisco, CA · On-site

As a Junior Research Scientist, you will work with a team of researchers to develop machine ... Responsibilities : • Develop and refine machine learning architectures optimized for advanced ...

Adobe

Sr. Machine Learning Engineer 4

San Jose, CA · On-site

$122K - $168K/yr

Mentor junior engineers and help grow the team's technical depth. What You Need to Succeed Required Qualifications * Master's or Ph.D. in Computer Science, Machine Learning, or a related technical ...

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Junior Machine Learning information

What is the difference between Junior Machine Learning vs Data Scientist?

AspectJunior Machine LearningData Scientist
Required CredentialsBachelor's in CS, Data Science, or related field; some experience with ML toolsBachelor's or Master's in CS, Statistics, or related; strong programming and statistical skills
Work EnvironmentEntry-level projects, supervised tasks, team collaborationAdvanced analysis, model development, cross-functional teams
Industry UsageCommon in tech companies, startups, research labsWidespread across industries like finance, healthcare, tech

Junior Machine Learning roles focus on foundational ML tasks and learning on the job, while Data Scientists handle complex data analysis, model building, and strategic insights. The roles differ mainly in experience level and scope of responsibilities, but both require strong technical skills and familiarity with data tools.

What does a junior machine learning engineer do?

A Junior Machine Learning Engineer assists in the development and implementation of machine learning models and algorithms under the supervision of more experienced engineers. They typically help with data collection, cleaning, feature engineering, model training, and evaluation. Junior engineers may also write code, test prototypes, and contribute to improving model performance while learning best practices in the field. Their role often involves collaborating with data scientists and software engineers to integrate machine learning solutions into products or services.

What types of projects and tasks can a junior machine learning professional typically expect to work on in their first year?

As a Junior Machine Learning professional, you’ll often support senior data scientists and engineers by preparing data, implementing basic algorithms, and assisting with model evaluation. Your daily tasks may include data cleaning, feature engineering, running experiments, and writing code to automate data pipelines. You might also help document processes and present your findings to team members. While the work is often collaborative, you’ll have opportunities to take ownership of smaller projects and progressively contribute to larger initiatives as you gain experience.

What are the key skills and qualifications needed to thrive as a junior machine learning engineer?

To thrive as a Junior Machine Learning Engineer, you need a solid understanding of programming (especially Python), basic statistics, linear algebra, and familiarity with machine learning concepts, typically supported by a relevant degree or coursework. Proficiency in tools and frameworks like scikit-learn, TensorFlow, PyTorch, and version control systems such as Git is often expected. Strong problem-solving abilities, curiosity, and effective communication are crucial soft skills for collaborating with teams and explaining technical concepts. These skills and qualities are important because they enable you to contribute effectively to building, testing, and improving machine learning models in real-world applications.
What are the most commonly searched types of Machine Learning jobs in California? The most popular types of Machine Learning jobs in California are:
What are popular job titles related to Junior Machine Learning jobs in California? For Junior Machine Learning jobs in California, the most frequently searched job titles are:
What job categories do people searching Junior Machine Learning jobs in California look for? The top searched job categories for Junior Machine Learning jobs in California are:
What cities in California are hiring for Junior Machine Learning jobs? Cities in California with the most Junior Machine Learning job openings:
Junior Machine Learning jobs near you
Infographic showing various Junior Machine Learning 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.

Senior Machine Learning Infrastructure Engineer, Simulation

Waymo

San Diego, CA • On-site

$115K - $156K/yr

Full-time

Re-posted 7 days ago

Job description

The Simulation ML Infrastructure team builds scalable AI/ML infrastructure to accelerate the Simulator team in sustainably innovating and building state of the art simulations of realistic environments for the testing and training of the Waymo Driver. To increase the fidelity and steerability of the simulations, we employ large foundation models trained on massive datasets to model the real world, including but not limited to, realistic agents (vehicles, pedestrians, cyclists, motorcyclists etc.), roads, traffic control systems, and weather etc.

We seek an experienced Senior Machine Learning Infrastructure Engineer to lead the development of advanced AI/ML infrastructure for multi-billion parameter foundation models in ML accelerator-friendly simulations. Your expertise in massive model scaling, ML accelerators, and distributed training will be required for designing and scaling our systems.

This role reports to an Engineering Manager.

You will:

  • Be part of a world-class, high-performing research engineering team to advance the state of the art of ultra realistic multi-agent simulations using foundation models.

  • Collaborate closely with the core Google DeepMind and Waymo Realism Modeling teams in London, and Waymo Oxford to use the large models to improve sim realism.

  • Provide deep technical leadership on large-scale ML model architectures, especially for autonomous vehicle models. Work at the intersection of data engineering, model development, and deployment, and provide guidance on architectural decisions and technical directions. Own large, complex systems, driving architectures that meet technical and business objectives.

  • Design and scale large distributed systems covering the ML lifecycle, supporting planet-scale dataset generation and model training.

  • Collaborate cross-functionally to derive performance and system-level requirements for large ML systems. Translate product/business goals into measurable technical deliverables, ensuring system component alignment.

  • Mentor junior engineers, growing their expertise and fostering a collaborative culture.

You have:

  • BS in Computer Science, Robotics, similar technical field of study, or equivalent practical experience

  • 5+ years of professional software engineering experience, with at least 3 years in machine learning infrastructure such as developing, scaling, training, deploying, and optimizing large-scale machine learning systems from data to model.

We prefer:

  • 10+ years of professional software engineering experience, with at least 5 years in machine learning infrastructure such as developing, designing, scaling, training, deploying, and optimizing large-scale machine learning systems from data to model.

  • Solid experience in the development and optimization of machine learning infrastructure tools like DeepSpeed, PyTorch, TensorFlow, or similar frameworks.

  • Strong expertise in distributed training techniques, including gradient sharding and optimization strategies for scaling large models across ML accelerator profiling tools to uncover performance bottlenecks.

  • Deep understanding of state-of-the-art machine learning models such as auto-regressive transformers and familiarity with custom-kernels for diverse h/w compute based efficiency.

  • Excellent communication skills, both verbal and written, with the ability to translate complex technical concepts for a broad audience.

  • Practical familiarity in Autonomous Driving, Simulations, and ML accelerators is a plus.

Apply