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Entry Level Google Machine Learning Engineer Jobs in Houston, TX

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Senior/Principal Machine Learning Engineer 200-300k Remote position possible Description * Develop solutions for autonomous driving, from experimentation to full commercialization. * Explore new ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

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Entry Level Google Machine Learning Engineer information

See Houston, TX salary details

$28.6K

$66.2K

$112.7K

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

As of May 29, 2026, the average yearly pay for entry level google machine learning engineer in Houston, TX is $66,239.00, according to ZipRecruiter salary data. Most workers in this role earn between $49,200.00 and $75,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an Entry Level Google Machine Learning Engineer, and why are they important?

To thrive as an Entry Level Google Machine Learning Engineer, you need a solid foundation in computer science, statistics, and mathematics, typically with at least a bachelor's degree in a related field. Familiarity with programming languages like Python or Java, experience using TensorFlow or PyTorch, and understanding of cloud platforms such as Google Cloud are essential technical requirements. Strong problem-solving skills, teamwork, and effective communication help you collaborate and convey complex concepts clearly. These skills and qualities are crucial for building scalable machine learning solutions and contributing effectively in a dynamic, innovative environment.

What are the typical projects and responsibilities for an Entry Level Google Machine Learning Engineer?

As an Entry Level Machine Learning Engineer at Google, you can expect to work on a variety of projects ranging from building and optimizing machine learning models to supporting data preprocessing and feature engineering tasks. You will often collaborate with senior engineers, data scientists, and product teams to implement solutions that address real-world problems at scale. Your daily responsibilities may include coding in Python or TensorFlow, participating in code reviews, and troubleshooting model performance. This role offers hands-on experience with industry-leading tools and the opportunity to learn from experienced colleagues, making it a great foundation for career growth in AI and machine learning.

What are Entry Level Google Machine Learning Engineers?

Entry Level Google Machine Learning Engineers are professionals who have recently started their careers in machine learning and work at Google. They typically assist in designing, developing, and deploying machine learning models to solve real-world problems. Their responsibilities may include data preprocessing, feature engineering, model training, evaluation, and collaborating with senior engineers and researchers. These roles often require a strong foundation in programming, mathematics, and statistics, as well as familiarity with machine learning frameworks such as TensorFlow or PyTorch. Entry Level Machine Learning Engineers at Google usually work on supervised projects and are mentored by more experienced team members.

What is the difference between Entry Level Google Machine Learning Engineer vs Entry Level Data Scientist?

AspectEntry Level Google Machine Learning EngineerEntry Level Data Scientist
Required CredentialsBachelor's in CS, Math, or related; knowledge of ML frameworksBachelor's in CS, Stats, or related; strong analytical skills
Work EnvironmentDeveloping ML models, deploying algorithms, coding in Python/JavaData analysis, statistical modeling, data visualization
Employer & Industry UsageTech companies, especially Google, focusing on AI/ML productsVarious industries including tech, finance, healthcare

Entry Level Google Machine Learning Engineers focus on developing and deploying machine learning models, often requiring coding and understanding of ML frameworks. Entry Level Data Scientists analyze data, build statistical models, and create visualizations. While both roles require similar educational backgrounds, their daily tasks and focus areas differ, with ML Engineers more involved in algorithm implementation and Data Scientists in data analysis and insights.

What job categories do people searching Entry Level Google Machine Learning Engineer jobs in Houston, TX look for? The top searched job categories for Entry Level Google Machine Learning Engineer jobs in Houston, TX are:

Senior Machine Learning Engineer - Deep & Reinforcement Learning

Kanak Elite Services Inc

Houston, TX โ€ข On-site

$99.80K - $137K/yr

Contractor

Posted 12 days ago


Job description

Hello There,

My name is Himanshu Sharma, and I serve as the Recruitment Lead at Kanak-IT INC. I am reaching out to share an excellent career opportunity for the role of Senior Machine Learning Engineer with our esteemed client. If you are interested then please share your updated resume at Himanshu01@kanakits.com .

Job Description

Positionย ย ย ย ย ย ย ย ย ย  : Senior Machine Learning Engineer โ€“ Deep & Reinforcement Learning

Locationย ย ย ย ย ย ย ย ย  : Houston, TXย Onsite

Durationย ย ย ย ย ย ย ย  : Long term contract

Required skills:
- Degree in STEM field, Ph.D preferred.
- Master in Deep Learning, Reinforcement Learning, and multimodal large language model.
- Strong Pytorch or TensorFlow programming
- Machine Learning and Statistical Modelling - Mastery
- Exploratory Analysis
- Core Programming Skills & Languages
- AI Engineering Essentials
- DevOps and Agile
- Cloud deployment frameworks, infrastructure, and tooling