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Deep Learning Developer Jobs in Houston, TX (NOW HIRING)

Corva

Senior Data Scientist

Houston, TX · On-site

This role combines applied research, production-grade ML engineering, and technical leadership. You will develop physics-based, rule-based, data-driven, and deep learning models that operate in real ...

Oxy

Senior AI - Computer Vision Engineer

Houston, TX · On-site

$117K - $154K/yr

Develop deep learning models using PyTorch and TensorFlow * Lead research and development (R&D) ... Collaborate closely with crossfunctional teams (AI platform, software engineering, domain experts ...

Oxy

Senior AI - Computer Vision Engineer

Houston, TX · On-site

$99K - $137K/yr

Develop deep learning models using PyTorch and TensorFlow * Lead research and development (R&D) ... Collaborate closely with cross-functional teams (AI platform, software engineering, domain experts ...

Augment

Principal Scientist, Data

Houston, TX · On-site

$80 - $83/hr

Design, train, fine-tune, and evaluate machine learning and deep learning models--including LLMs--for predictive analytics and automated decision-making. Data Engineering & Feature Development

The Jacobs Brand

GIS Developer

Houston, TX · On-site +1

Software scripting, command line skills, automation of tasks, programming language - Machine learning, deep learning, neural networks * Mission science integration and operations * Previous work with ...

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How much do deep learning developer jobs pay per hour?

As of Jun 7, 2026, the average hourly pay for deep learning developer in Houston, TX is $36.71, according to ZipRecruiter salary data. Most workers in this role earn between $31.20 and $40.87 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Deep Learning Developer, and why are they important?

To thrive as a Deep Learning Developer, you need a strong background in computer science, mathematics, and proficiency in programming languages like Python, often supported by a degree in a related field. Familiarity with deep learning frameworks such as TensorFlow or PyTorch, and experience with cloud platforms or GPU acceleration, are commonly required technical skills. Analytical thinking, problem-solving abilities, and effective teamwork distinguish top performers in this role. These competencies are crucial for designing, training, and deploying advanced neural network models that address complex real-world problems.

What are Deep Learning Developers?

Deep Learning Developers are specialized software engineers or data scientists who design, build, and implement artificial intelligence systems using deep learning techniques. They work with neural networks, large datasets, and various frameworks like TensorFlow or PyTorch to develop models for tasks such as image recognition, natural language processing, and autonomous systems. Their responsibilities include data preprocessing, model training, optimization, and deployment to solve complex problems that require advanced pattern recognition. Deep Learning Developers often collaborate with AI researchers, data engineers, and product teams to integrate intelligent features into applications.

Which 3 jobs will survive AI?

Deep Learning Developers are likely to continue to be in demand as AI advances because they design and improve AI models, requiring specialized skills in programming, mathematics, and data analysis. Other resilient roles include AI ethicists, who address ethical considerations, and AI system trainers, who curate and annotate data to improve AI performance. These jobs involve complex problem-solving and human oversight that are less easily automated.

What is the difference between Deep Learning Developer vs Machine Learning Engineer?

AspectDeep Learning DeveloperMachine Learning Engineer
Required CredentialsBachelor's or Master's in CS, AI, or related; experience with neural networksBachelor's or Master's in CS, Data Science, or related; knowledge of algorithms
Work EnvironmentResearch labs, AI startups, tech companies focusing on neural networksData-driven companies, software firms, industries applying machine learning
Industry UsagePrimarily in AI research, neural network development, deep learning projectsBroader application including predictive modeling, data analysis, and ML systems

Deep Learning Developers specialize in neural networks and deep learning models, often working on AI research and complex algorithms. Machine Learning Engineers have a broader focus on developing, deploying, and maintaining machine learning models across various applications. While both roles require similar educational backgrounds, their focus areas and industry applications differ.

What are some common challenges Deep Learning Developers face when deploying models to production environments?

Deep Learning Developers often encounter challenges such as optimizing model performance for real-time inference, managing resource constraints (like GPU/CPU availability), and ensuring model reproducibility across different environments. Additionally, integrating deep learning models into existing software systems and maintaining them over time can be complex, especially as data and requirements evolve. Collaborating closely with DevOps, data engineers, and QA teams is essential to address these challenges and ensure smooth deployment and ongoing reliability.
What cities near Houston, TX are hiring for Deep Learning Developer jobs? Cities near Houston, TX with the most Deep Learning Developer job openings:
Deep Learning Developer jobs near you
Infographic showing various Deep Learning Developer job openings in Houston, TX as of May 2026, with employment types broken down into 35% Full Time, 59% Part Time, 5% Contract, and 1% Nights. Highlights an 79% Physical, 5% Hybrid, and 16% Remote job distribution, with an average salary of $76,356 per year, or $36.7 per hour.

Software Engineer, Machine Learning Infrastructure

Bot Auto

Houston, TX • On-site

$165K - $195K/yr

Other

Posted 28 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