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Internship Full Stack Machine Learning Engineer Jobs in Houston, TX

Collabera LLC

Full Stack developer

Houston, TX · On-site

$75 - $85/hr

Full Stack Developer Location: Spring, TX (77389) - 100% onsite! Contract: 6 months contract with possibility of extension Pay Range: $75 - $85 per hour Benefits: The Company offers the following ...

New

SynMax

Senior Full-Stack Engineer

Houston, TX · Remote

The Role We are looking for a Senior Full-Stack Engineer who embraces AI agents to supercharge ... Continuous Learning: Mentorship from technical leaders How To Apply Please submit your resume and ...

SynMax

Senior Full-Stack Engineer

Houston, TX · On-site +1

The Role We are looking for a Senior Full-Stack Engineer who embraces AI agents to supercharge ... Continuous Learning: Mentorship from technical leaders How To Apply Please submit your resume and ...

Apolis

Full Stack Developer

Spring, TX · On-site

Fullstack Developer Spring, TX Long Term contract A full-stack web developer is responsible for the development and maintenance of server-side logic, integration of their application elements, and ...

New

DataAnnotation

SDLC Engineer - AI Trainer

Conroe, TX · Remote

$50 - $100/hr

As a DataAnnotation's coder, you'll be part of a growing community of over 100,000 professionals -- including front-end, back-end, full-stack, machine learning, and other engineers -- who are driving ...

DataAnnotation

SDLC Engineer - AI Trainer

Sugar Land, TX · Remote

$50 - $100/hr

As a DataAnnotation's coder, you'll be part of a growing community of over 100,000 professionals -- including front-end, back-end, full-stack, machine learning, and other engineers -- who are driving ...

DataAnnotation

SDLC Engineer - AI Trainer

Houston, TX · Remote

$50 - $100/hr

As a DataAnnotation's coder, you'll be part of a growing community of over 100,000 professionals -- including front-end, back-end, full-stack, machine learning, and other engineers -- who are driving ...

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All JobsInternship Full Stack Machine Learning Engineer JobsInternship Full Stack Machine Learning Engineer Jobs in Houston, TX

Internship Full Stack Machine Learning Engineer information

See Houston, TX salary details

$42.5K

$128.7K

$181.9K

How much do internship full stack machine learning engineer jobs pay per year?

As of Jun 26, 2026, the average yearly pay for internship full stack machine learning engineer in Houston, TX is $128,702.00, according to ZipRecruiter salary data. Most workers in this role earn between $106,000.00 and $150,900.00 per year, depending on experience, location, and employer.

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

Software Engineer, Machine Learning Infrastructure

Bot Auto

Houston, TX

$165K - $195K/yr

Other

Posted 17 days ago

Job description

Company Introduction

At Bot Auto, we are revolutionizing the transportation of goods with our cutting-edge autonomous trucks, enhancing the quality of life for communities around the globe. With the agility of a start-up and the wisdom of seasoned experts, Bot Auto boasts a team that has achieved numerous world-firsts and unparalleled innovations. United by a shared vision, we create miracles and propel the future of transportation. Join us and transform your dreams into reality.

We are seeking a highly skilled and motivated Software Engineer to design, develop, and scale our machine learning annotation, evaluation, and training infrastructure. This role is central to the quality and velocity of our perception and ML models - from curating and managing high-quality annotated datasets, to building robust evaluation pipelines that drive continuous model improvement. The ideal candidate combines strong systems engineering skills with a deep understanding of ML Workflows/Ops and large-scale data infrastructure.

Key Responsibilities

Machine Learning & Deep Learning Infrastructure

  • Evaluation Platform - Architect and own a scalable, end-to-end model evaluation platform for perception and prediction models central to autonomous driving. Define metrics, design for scale, and make results actionable for researchers.
  • Training Infrastructure - Partner with research scientists to optimize and scale distributed training workflows. Integrate experiment tracking and reproducibility into the model lifecycle from day one.
  • Dataset & Feature Store - Design and maintain a versioned, high-quality training data store that accelerates model development and supports rapid iteration.
  • ML Pipelines - Build automated pipelines spanning data preparation, model training, validation, and deployment - enabling fast experimentation and reproducible outcomes.
  • Annotation Platform - Contribute to tooling and infrastructure that powers high-throughput, high-accuracy data annotation at scale.
  • MLOps - Develop production ML services that treat models as products - with reliability, observability, and continuous improvement built in.

Data Infrastructure

  • Maintain and evolve a robust data storage and access layer (S3 data lake, Delta Lake) underpinning annotation, evaluation, and training workflows.
  • Build scalable, reliable data collection pipelines supporting diverse vehicle dispatch missions.
  • Develop foundational services and packages that provide clean, performant access to autonomous driving data across the stack.
Qualifications

Required:

  • Educational Background: Bachelor's or Master's in Computer Science, or equivalent practical experience.
  • Strong Programming Skills: Strong proficiency in Python; working knowledge of C++
  • ML/DL Infrastructure Experience - Demonstrated hands-on experience building or scaling at least one of the following in a production environment:
    • Evaluation platforms - automated model benchmarking, metric computation, and regression tracking across model versions.
    • Training infrastructure - distributed training pipelines, experiment tracking, and model lifecycle management (e.g. W&B, MLflow, ClearML).
    • Dataset curation & feature stores - versioned dataset management, data lineage, and tooling for high-quality training data at scale.
    • Annotation platforms - tooling or pipelines that support high-throughput, high-accuracy labeling workflows.
  • Distributed Systems - Strong experience with distributed computing and container orchestration - Kubernetes, Spark, or comparable frameworks.
  • Ability to operate independently: scope ambiguous problems, make sound architecture decisions, and drive them to completion.

Preferred:

  • C++ experience in performance-sensitive or safety-critical applications
  • Full-stack service development experience.
  • Prior work in autonomous driving or robotics.

Apply