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Entry Level Machine Learning Engineer Jobs in Mountain View, CA

Jobzhr

Machine Learning Engineer

Pleasanton, CA · On-site

$110 - $150/hr

... with machine learning frameworks such as TensorFlow, Keras, and PyTorch. Knowledge of cloud platforms and technologies, specifically Microsoft Azure, is crucial. Experience in DevOps and MLOps ...

New

Amat

Machine Learning Engineer

Santa Clara, CA · On-site +1

$184K - $253K/yr

We're looking for a seasoned machine learning engineer with a deep ML foundation who has actively kept pace with the field-someone equally comfortable with classical ML and the latest generative ...

Relace

Machine Learning Engineer

San Francisco, CA · On-site

The Role We're looking for a Machine Learning Engineer who loves getting close to the metal. This is a hands-on engineering role focused on making models faster, more efficient, and more reliable ...

Aven

Machine Learning Engineer

Campbell, CA · On-site

We are looking for Machine Learning Engineers who have built product models from idea to delivery. You are passionate about digging into data, cleaning it, analyzing it, generating ideas, and ...

Match Group

Machine Learning Engineer II

Palo Alto, CA · On-site +1

$145K - $165K/yr

Machine Learning Engineers (this role) who focus on modeling and algorithmic innovation * Machine Learning Infrastructure Engineers who build the platforms and tools that enable scalable training ...

Match Group

Machine Learning Engineer II

Palo Alto, CA · On-site +1

$114K - $156K/yr

Machine Learning Engineers (this role) who focus on modeling and algorithmic innovation * Machine Learning Infrastructure Engineers who build the platforms and tools that enable scalable training ...

Match Group

Machine Learning Engineer II

Palo Alto, CA · On-site

$165K/yr

Machine Learning Engineers (this role) who focus on modeling and algorithmic innovation * Machine Learning Infrastructure Engineers who build the platforms and tools that enable scalable training ...

Tinder

Machine Learning Engineer II

Palo Alto, CA · On-site

$145 - $165/hr

Machine Learning Engineers (this role) who focus on modeling and algorithmic innovation * Machine Learning Infrastructure Engineers who build the platforms and tools that enable scalable training ...

New

Apple Inc.

Machine Learning Engineer (Search)

Cupertino, CA · On-site

$147.40 - $220.90/hr

We are looking for a Machine Learning Engineer to join and play a big part in the next revolution of Maps; to enable users to find more things in innovative ways. On our team, you will have plenty of ...

Adobe, Inc.

Machine Learning Engineer

San Jose, CA · On-site

... more programming languages such as Python, Java * Strong understanding and working knowledge of distributed systems, isolation and micro services * Good understanding of machine learning, deep ...

Adobe

Machine Learning Engineer

San Jose, CA · On-site

... more programming languages such as Python, Java * Strong understanding and working knowledge of distributed systems, isolation and micro services * Good understanding of machine learning, deep ...

Apple

Sr. Machine Learning Engineer

Santa Clara, CA · On-site

$143K - $189K/yr

Our team comprises a diverse range of backgrounds, including applied machine learning engineers with a focus on ML and LLM, and experienced distributed systems engineers. As such, we are seeking ...

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All JobsMachine Learning Engineer JobsEntry Level Machine Learning Engineer JobsEntry Level Machine Learning Engineer Jobs in Mountain View, CA

Entry Level Machine Learning Engineer information

See Mountain View, CA salary details

$35.4K

$81.8K

$139.2K

How much do entry level machine learning engineer jobs pay per year?

As of Aug 7, 2026, the average yearly pay for entry level machine learning engineer in Mountain View, CA is $81,825.00, according to ZipRecruiter salary data. Most workers in this role earn between $60,800.00 and $92,600.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an entry level machine learning engineer?

To thrive as an Entry Level Machine Learning Engineer, you need a solid understanding of machine learning algorithms, programming languages like Python, and a degree in computer science, engineering, or a related field. Familiarity with tools such as TensorFlow, PyTorch, scikit-learn, and version control systems like Git is highly valuable, and completing online courses or certifications can further demonstrate your skills. Strong analytical thinking, attention to detail, and effective communication are important soft skills in this role. These abilities are essential because they enable you to build accurate models, work collaboratively with teams, and communicate insights to stakeholders.

What are some typical projects or tasks an entry level machine learning engineer might work on?

As an Entry Level Machine Learning Engineer, you’ll often work on tasks such as data preprocessing, feature engineering, and assisting in training and evaluating models under the guidance of senior engineers or data scientists. You may help develop prototypes, automate data collection pipelines, and collaborate with software engineers to integrate machine learning solutions into products. Working in this role typically involves frequent collaboration in a team environment, participating in code reviews, and learning best practices for scalable model deployment. These foundational experiences are designed to build your technical expertise and set the stage for future growth within the field.

What is an entry level machine learning engineer?

An Entry Level Machine Learning Engineer is responsible for developing, testing, and deploying machine learning models under the guidance of senior engineers. They work with datasets, implement algorithms, and optimize model performance. Their role often involves data preprocessing, feature engineering, and collaborating with data scientists and software engineers. Strong programming skills in Python, knowledge of ML frameworks like TensorFlow or PyTorch, and an understanding of statistics and algorithms are essential. This position serves as a foundation for building expertise in artificial intelligence and data-driven decision-making.

What are the most commonly searched types of Machine Learning Engineer jobs in Mountain View, CA? The most popular types of Machine Learning Engineer jobs in Mountain View, CA are:
What are popular job titles related to Entry Level Machine Learning Engineer jobs in Mountain View, CA? For Entry Level Machine Learning Engineer jobs in Mountain View, CA, the most frequently searched job titles are:
What job categories do people searching Entry Level Machine Learning Engineer jobs in Mountain View, CA look for? The top searched job categories for Entry Level Machine Learning Engineer jobs in Mountain View, CA are:
What cities near Mountain View, CA are hiring for Entry Level Machine Learning Engineer jobs? Cities near Mountain View, CA with the most Entry Level Machine Learning Engineer job openings:
Entry Level Machine Learning Engineer jobs near you
Infographic showing various Entry Level Machine Learning Engineer job openings in Mountain View, CA as of August 2026, with employment types broken down into 33% Internship, and 67% Full Time. Highlights an 33% In-person, 34% Hybrid, and 33% Remote job distribution, with an average salary of $81,825 per year, or $39.3 per hour.

Machine Learning Engineer

Human Archive

San Francisco, CA • On-site

$100K - $150K/yr

Full-time

Re-posted 16 days ago

Job description

About Human Archive
Human Archive is a research lab focused on modeling human embodied intelligence.
Humans are the most sophisticated biological systems we have ever observed, yet we still do not fully understand ourselves. Research into human physical intelligence - including the human hand, proprioception, and vision - remains largely unsolved. Our mission is to recover human embodied intelligence as a learned model. To achieve this, we build custom hardware products, deploy them globally at scale, and publish research. Today, our data is used for robotics and world modeling, but the broader opportunity is advancing scientific research into intelligence itself.
Founded by Stanford and UC Berkeley researchers, we are lean, deeply technical, and operate at extreme speed, taking on unglamorous and conventionally impossible problems that directly unlock step-function gains in model capability.
The deployment of capable humanoids at scale will permanently redefine human labor. Undesirable physical work will disappear, and human effort will shift toward a new era of abundant creativity.
We are building the infrastructure to accelerate that transition by assembling the Human Archive mafia. You will own meaningful systems from day one and see your work directly impact model capabilities. This is a once-in-a-generation inflection point. If you want to help reshape physical labor and work on problems that matter at civilizational scale, join us.
The Opportunity
As a Machine Learning Engineer, you'll work on multimodal perception, VLA training, robotics post-training, and downstream policy evaluation. This is a hands-on role at the intersection of applied machine learning, data infrastructure, and robotics, where your work directly shapes how data is collected, validated, annotated, and evaluated.
You'll help close the loop between research and data collection by fine-tuning VLAs on downstream policy performance and building post-training and reinforcement learning systems around real-world robotics tasks. You'll be expected to make architectural decisions, own projects end-to-end, and operate in highly ambiguous research environments given the novelty and scale of our multimodal datasets.
Your work will help shape how frontier labs and leading robotics companies train their models, transforming physical labor markets and economies while contributing to broader research into human embodied intelligence.
What You'll Do
  • Build systems for multimodal perception, annotation, dataset QA, and robotics evaluation
  • Publish research on multimodal data by fine-tuning and evaluating VLA models on downstream robotics tasks and policy performance
  • Build post-training and reinforcement learning systems around robotics failure modes and corrective demonstrations
  • Work across video understanding, tracking, pose estimation, temporal modeling, and multimodal alignment
  • Develop tooling for benchmarking, observability, and temporal efficiency
  • Prototype quickly, ship rapidly, and iterate from real-world robotics deployments and research feedback
What We're Looking For
  • Passionate, mission-driven individuals who have demonstrated exceptional ownership in previous work
  • Engineers who want their work to directly impact the next frontier of physical AGI
  • Strong ML engineering fundamentals across robotics, computer vision, and perception systems
  • Experience with video understanding, tracking, pose estimation, robotics, or real-world sensor systems
  • Strong technical intuition and ability to move quickly in ambiguous research environments
  • Published research or production experience in robotics, embodied AI, reinforcement learning, motion capture, or vision systems is a strong plus

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