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

Bot Auto

Intern, Deep Learning Engineer

Houston, TX

$14.25 - $19/hr

Train, tune, and optimize deep learning models using our large-scale compute clusters and truck ... Engineering Plus: Experience with Linux, Git, C++, or deployment tools like TensorRT/ONNX.

Bot Auto

Intern, Deep Learning Engineer

Houston, TX · On-site

$14.25 - $19/hr

Train, tune, and optimize deep learning models using our large-scale compute clusters and truck ... Engineering Plus: Experience with Linux, Git, C++, or deployment tools like TensorRT/ONNX.

Pivotal Solutions

Senior AI Engineer

Houston, TX · On-site

$99K - $137K/yr

Expertise in AI/ML algorithms, deep learning frameworks (e.g., TensorFlow, PyTorch), and the end-to ... Strong programming skills in Python and proficiency with relevant libraries (e.g., NumPy, Pandas ...

Beth Page tech

Senior Machine Learning Engineer

Houston, TX · On-site

$99K - $137K/yr

Senior Machine Learning Engineer Location: Houston, TX Environment: Standard, 5-days onsite : Must ... Deep Neural Networks (DNN): * Hands-on experience with CNN, RNN, Graph Neural Networks, and ...

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Deep Learning Engineer information

See Houston, TX salary details

$36.3K

$110.6K

$182.9K

How much do deep learning engineer jobs pay per year?

As of Aug 23, 2026, the average yearly pay for deep learning engineer in Houston, TX is $110,647.00, according to ZipRecruiter salary data. Most workers in this role earn between $79,300.00 and $144,700.00 per year, depending on experience, location, and employer.

What is a deep learning engineer?

A Deep Learning Engineer is a specialized software engineer who designs, develops, and optimizes deep learning models. They work with neural networks, large datasets, and frameworks like TensorFlow or PyTorch to build AI systems for tasks like image recognition, natural language processing, and autonomous systems. Their responsibilities include data preprocessing, model training, performance tuning, and deploying models into production. Strong programming skills in Python, knowledge of machine learning algorithms, and experience with GPU acceleration are essential for this role.

What does a deep learning engineer do?

Deep Learning Engineers typically spend their days designing, developing, and optimizing neural network models for tasks like image recognition, natural language processing, or recommendation systems. They preprocess and analyze large datasets, experiment with model architectures, and tune hyperparameters to achieve the best performance. Collaboration is often required with data scientists, product managers, and software engineers to integrate models into real-world applications and scale solutions for production. Additionally, many deep learning engineers review current research, stay updated on advancements in AI, and continuously improve their skills. This role offers a dynamic work environment where learning and innovation are highly encouraged.

What skills and qualifications does a deep learning engineer need?

To thrive as a Deep Learning Engineer, you need a strong background in mathematics, machine learning theory, and programming (especially Python), often supported by a relevant degree in computer science, engineering, or related fields. Proficiency with frameworks such as TensorFlow, PyTorch, Keras, as well as experience with GPUs and cloud platforms, is highly valued, and certifications in AI or deep learning can further enhance your profile. Effective problem-solving, strong collaboration skills, and clear communication are important soft skills for excelling in interdisciplinary teams. These abilities ensure that you can develop robust deep learning models, adapt to evolving technologies, and contribute value in both technical and collaborative settings.

Are deep learning engineers in demand?

Deep learning engineers are in high demand due to the growth of artificial intelligence and machine learning applications across industries such as technology, healthcare, and finance. They typically require skills in neural networks, programming languages like Python, and frameworks such as TensorFlow or PyTorch, with job opportunities increasing as AI adoption expands.

What are popular job titles related to Deep Learning Engineer jobs in Houston, TX?

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

What job categories do people searching Deep Learning Engineer jobs in Houston, TX look for?

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

What cities near Houston, TX are hiring for Deep Learning Engineer jobs?

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

Deep Learning Engineer jobs near you
Infographic showing various Deep Learning Engineer job openings in Houston, TX as of August 2026, with employment types broken down into 1% As Needed, 77% Full Time, 21% Part Time, and 1% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $110,647 per year, or $53.2 per hour.

Intern, Deep Learning Engineer

Bot Auto

Houston, TX

$14.25 - $19/hr

Full-time, Internship

Re-posted 22 days ago

Job description

About Bot Auto 

Bot Auto is revolutionizing autonomous trucking by combining start-up agility with the wisdom of seasoned experts. We are looking for MS/PhD interns to join our core AI team for a 3-6 month internship to tackle real-world edge cases.

Key Responsibilities
  • SOTA Prototyping: Implement and benchmark next-gen architectures (e.g., Multi-modal perception, Online Mapping, Behavior Prediction, World Model).
  • Project Ownership: Own a targeted research project from data analysis to model verification under senior mentorship.
  • Scale Experimentation: Train, tune, and optimize deep learning models using our large-scale compute clusters and truck datasets.
Qualifications

Required:

  • Education: Current Master's or Ph.D. candidate in CS, Robotics, or a related field, specifically focusing on Deep Learning, Computer Vision, Robotics, or related fields. 
  • Technical Stack: Proficient in Python and PyTorch with clean coding practices.
  • Theoretical Core: Solid understanding of modern AI architectures, especially Transformers and its applications in different fields.
  • Commitment: Available full-time for at least 3 months.

Preferred:

  • Research Focus: Academic thesis or project experience in Multi-sensor Perception, Generative AI/Diffusion, Motion Prediction, or End-to-End Autonomous Driving.
  • Track Record: Publications or submissions at top conferences (e.g., CVPR, ICCV, NeurIPS, ICLR, ICRA).
  • Engineering Plus: Experience with Linux, Git, C++, or deployment tools like TensorRT/ONNX.

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