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Junior Machine Learning Engineer Jobs in Houston, TX

Varsity Tutors

Machine Learning Tutor

Missouri City, TX · Remote

$18 - $40/hr

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

Varsity Tutors

Machine Learning Tutor

Sugar Land, TX · Remote

$18 - $40/hr

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

Varsity Tutors

Machine Learning Tutor

Pearland, TX · Remote

$18 - $40/hr

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

Varsity Tutors

Machine Learning Tutor

Houston, TX · Remote

$18 - $40/hr

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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All JobsMachine Learning Engineer JobsJunior Machine Learning Engineer JobsJunior Machine Learning Engineer Jobs in Houston, TX

Junior Machine Learning Engineer information

See Houston, TX salary details

$31.4K

$67.4K

$102.7K

How much do junior machine learning engineer jobs pay per year?

As of Sep 8, 2026, the average yearly pay for junior machine learning engineer in Houston, TX is $67,365.00, according to ZipRecruiter salary data. Most workers in this role earn between $45,500.00 and $75,100.00 per year, depending on experience, location, and employer.

What does a junior machine learning engineer do?

As a junior machine learning engineer, you work in AI, performing research with algorithms and data modeling techniques. Machine learning involves using large collections of data to create systems that are capable of making predictions, and in this field, your duties and responsibilities revolve around using advanced mathematics to design applications for use in everything from stock trading to sports betting. Some machine learning efforts involve images, and this branch of the field is known as computer vision, while other techniques which focus on text are called natural language processing (NLP). Given these divisions, titles in machine learning include computer vision engineer, NLP scientist, or simply research scientist.

What kinds of projects and responsibilities can a junior machine learning engineer expect in their first year on the job?

As a Junior Machine Learning Engineer, you’ll typically work on tasks such as data preprocessing, building and testing simple models, and supporting more senior engineers in deploying machine learning solutions. Your responsibilities may also include cleaning datasets, implementing basic algorithms, and running experiments to evaluate model performance. You’ll often collaborate closely with data scientists, software engineers, and product teams to understand project goals and learn best practices. The role provides excellent opportunities to develop your technical skills, gain exposure to various stages of the ML pipeline, and gradually take on more complex projects as you grow.

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

To succeed as a Junior Machine Learning Engineer, you need a solid grasp of programming (especially Python), foundational knowledge of algorithms and statistics, and a relevant degree in computer science, mathematics, or a related field. Familiarity with machine learning frameworks such as TensorFlow or PyTorch and tools like scikit-learn, as well as experience with version control systems like Git, are typically required. Strong problem-solving abilities, attention to detail, and a willingness to learn from feedback are valuable soft skills that help you adapt and grow in the field. These skills ensure you can effectively develop, test, and improve machine learning models while collaborating with more experienced engineers and contributing to team projects.

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

AspectJunior Machine Learning EngineerData Scientist
Required CredentialsBachelor's in CS, Data Science, or related; some experience with ML frameworksBachelor's or higher in CS, Statistics, or related; often advanced certifications
Work EnvironmentDeveloping and deploying ML models, coding, testingData analysis, statistical modeling, interpreting data insights
Employer & Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, tech, consulting
Search & Comparison IntentYesYes

While both roles involve working with data and machine learning, Junior Machine Learning Engineers focus on building and deploying models, often with coding and engineering skills. Data Scientists analyze data, create statistical models, and interpret insights. The roles overlap but differ mainly in their core responsibilities and skill emphasis.

How much do junior machine learning engineers make?

Junior machine learning engineers typically earn between $70,000 and $100,000 annually, depending on location, education, and industry. Entry-level roles often require knowledge of programming languages like Python and familiarity with machine learning frameworks such as TensorFlow or PyTorch.
More about Junior Machine Learning Engineer jobs

What are the most commonly searched types of Machine Learning Engineer jobs in Houston, TX?

The most popular types of Machine Learning Engineer jobs in Houston, TX are:

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For Junior Machine Learning Engineer jobs in Houston, TX, the most frequently searched job titles are:

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The top searched job categories for Junior Machine Learning Engineer jobs in Houston, TX are:

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Cities near Houston, TX with the most Junior Machine Learning Engineer job openings:

Junior Machine Learning Engineer jobs near you
Infographic showing various Junior Machine Learning Engineer job openings in Houston, TX as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 22% Part Time, and 1% Contract. Highlights an 89% Physical, 2% Hybrid, and 9% Remote job distribution, with an average salary of $67,365 per year, or $32.4 per hour.

Senior Data Scientist /Senior Machine Learning Engineer/ AI/ML Engineer

Medinext Global LLC

Houston, TX • On-site

$125K - $150K/yr

Other

Posted 12 days ago

Key responsibilities

  • Develop, train, evaluate, and deploy machine learning and predictive models to solve complex business problems.

  • Design and implement Generative AI and LLM-based solutions for enterprise applications.

  • Collaborate with product, engineering, and business teams to integrate AI/ML solutions into enterprise products.

Job description

Senior Data Scientist – GenAI / RAG

Location: Houston, TX
Employment Type: Full-Time
Experience: 7–15 Years
Salary: $125,000 – $150,000 per year
Work Authorization: ,

Client: Tavant Technologies
Industry: Information Technology / Enterprise Products

Job Summary

Tavant Technologies is seeking a Senior Data Scientist – GenAI / RAG to join its Enterprise Products team. The ideal candidate will have a strong foundation in traditional Data Science and Machine Learning, combined with hands-on experience developing Generative AI, Large Language Model (LLM), Retrieval-Augmented Generation (RAG), and Agentic AI solutions.

The candidate should be experienced in applying advanced analytical and machine learning techniques to complex business problems, working with large datasets, and translating data-driven insights into scalable enterprise solutions.

Experience in the Energy, Utilities, Oil & Gas, Renewable Energy, or Natural Resources domain is highly preferred.

The successful candidate should also be comfortable collaborating with product, engineering, and business teams and communicating technical concepts effectively to both technical and non-technical stakeholders.

Key Responsibilities
  • Develop, train, evaluate, and deploy machine learning and predictive models to solve complex business problems.
  • Apply statistical analysis and advanced data science techniques to generate actionable business insights.
  • Design and implement Generative AI and LLM-based solutions for enterprise applications.
  • Develop and enhance RAG pipelines for enterprise knowledge retrieval and question-answering use cases.
  • Contribute to Agentic AI workflows and intelligent enterprise solutions where applicable.
  • Analyze large and complex datasets to identify trends, patterns, opportunities, and business risks.
  • Collaborate with Product Managers, Software Engineers, Data Engineers, and business stakeholders to integrate AI/ML solutions into enterprise products.
  • Develop scalable data science solutions using modern cloud and big-data technologies.
  • Evaluate model performance and continuously improve accuracy, reliability, and scalability.
  • Communicate analytical findings, model results, and recommendations clearly to technical and non-technical stakeholders.
  • Support production deployment, monitoring, troubleshooting, and optimization of ML and GenAI solutions.
  • Stay current with emerging developments in Machine Learning, Generative AI, LLMs, RAG, and data science technologies.
  • Provide technical guidance and mentorship to junior data scientists when required.
Required Qualifications
  • 7+ years of professional experience in Data Science / Machine Learning.
  • Strong programming experience with Python or R.
  • Strong understanding of Machine Learning, statistical modeling, and predictive analytics.
  • Hands-on experience with machine learning frameworks such as:
    • Scikit-learn
    • XGBoost
    • CatBoost
    • TensorFlow
    • PyTorch
  • Hands-on experience with Generative AI and Large Language Models (LLMs).
  • Strong practical experience developing RAG / Retrieval-Augmented Generation solutions.
  • Experience with LLM evaluation, LLMOps, or MLOps is highly desirable.
  • Experience with big-data technologies such as Databricks, Snowflake, Spark, or PySpark.
  • Strong SQL and database experience.
  • Experience working with large-scale datasets and data pipelines.
  • Experience with at least one major cloud platform such as AWS, Azure, or Google Cloud.
  • Experience with data visualization tools such as Power BI, Tableau, or similar platforms.
  • Strong analytical, problem-solving, and critical-thinking skills.
  • Excellent written and verbal communication skills.
Preferred Qualifications
  • Experience in the Energy / Utilities / Oil & Gas / Renewable Energy / Natural Resources industry.
  • Experience supporting enterprise products or large-scale enterprise applications.
  • Experience with Agentic AI / AI Agents and frameworks such as LangChain or LangGraph.
  • Experience with vector databases and semantic search.
  • Experience with ML model deployment, monitoring, and lifecycle management.
  • Master''s or Ph.D. in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative discipline.
  • Experience working directly with customers or business stakeholders.
Preferred Industry Background

Candidates with experience supporting organizations in the following areas are highly preferred:

Utilities / Grid

  • Duke Energy
  • NextEra Energy
  • Southern Company
  • Exelon
  • National Grid
  • PG&E

Oil & Gas / Natural Resources

  • Schlumberger / SLB
  • Halliburton
  • Chevron
  • ConocoPhillips

Energy Technology

  • Hanwha Qcells

Consulting – Energy Practices

  • Accenture
  • Deloitte
  • Capgemini
Core Technical Skills

Data Science:
Python, R, SQL, Statistical Modeling, Predictive Analytics, Machine Learning

Machine Learning:
Scikit-learn, XGBoost, CatBoost, TensorFlow, PyTorch

Generative AI:
GenAI, LLMs, RAG, Retrieval-Augmented Generation, Agentic AI

Big Data:
Databricks, Snowflake, Apache Spark, PySpark

MLOps / LLMOps:
MLflow, Model Evaluation, Model Monitoring, Model Deployment

Cloud:
AWS, Azure, Google Cloud

Visualization:
Power BI, Tableau

Ideal Candidate Profile

The ideal candidate is not purely a GenAI/LLM engineer or an academic Data Scientist. We are looking for someone who combines:

Traditional Data Science + Machine Learning + GenAI/LLM + RAG + Enterprise Product Experience

Candidates with direct Energy-domain experience and the ability to communicate effectively with customers and business stakeholders will receive strong preference.

Please submit candidates with recent, hands-on experience in Data Science, Machine Learning, and GenAI/RAG.

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