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Deep Learning Ai Jobs in Spring, TX (NOW HIRING)

Bot Auto

Intern, Deep Learning Engineer

Houston, TX

$14.25 - $19/hr

We are looking for MS/PhD interns to join our core AI team for a 3-6 month internship to tackle ... Train, tune, and optimize deep learning models using our large-scale compute clusters and truck ...

Bot Auto

Intern, Deep Learning Engineer

Houston, TX · On-site

$14.25 - $19/hr

We are looking for MS/PhD interns to join our core AI team for a 3-6 month internship to tackle ... Train, tune, and optimize deep learning models using our large-scale compute clusters and truck ...

Varsity Tutors

Machine Learning Tutor

Houston, TX · Remote

$18 - $40/hr

Our AI-powered Tutor Copilot enhances your sessions with real-time instructional support, lesson ... Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ...

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All JobsDeep Learning Ai JobsDeep Learning Ai Jobs in Spring, TX

Deep Learning Ai information

See Spring, TX salary details

$9.8K

$74.6K

$124.6K

How much do deep learning ai jobs pay per year?

As of Aug 22, 2026, the average yearly pay for deep learning ai in Spring, TX is $74,649.00, according to ZipRecruiter salary data. Most workers in this role earn between $64,100.00 and $123,700.00 per year, depending on experience, location, and employer.

What is a deep learning AI professional?

Deep Learning AI professionals are experts who design, develop, and implement artificial intelligence systems that use deep neural networks to analyze complex data and solve tasks such as image recognition, natural language processing, and autonomous decision-making. They work with large datasets and advanced algorithms to build models that can learn and improve over time. These professionals often have a background in computer science, mathematics, or engineering, and are skilled in programming languages like Python and frameworks such as TensorFlow or PyTorch.

What are the key skills and qualifications needed to thrive as a deep learning AI engineer?

To thrive as a Deep Learning AI Engineer, you need a strong background in mathematics, programming (especially Python), and experience with neural networks, typically supported by a degree in computer science, engineering, or a related field. Proficiency with deep learning frameworks such as TensorFlow or PyTorch, and knowledge of tools like CUDA for GPU acceleration, are essential; relevant certifications can be advantageous. Analytical thinking, creativity, and effective communication are important soft skills for solving complex problems and collaborating with cross-functional teams. These skills and qualities are crucial for building robust AI models and driving innovation in this rapidly evolving field.

What are some common challenges faced by professionals working in deep learning AI, and how can they be addressed?

Professionals in Deep Learning AI often encounter challenges such as managing large datasets, ensuring model accuracy, and addressing issues like overfitting. Collaboration with data engineers and domain experts is crucial to ensure high-quality data and relevant feature selection. Additionally, staying up-to-date with rapidly evolving frameworks and algorithms requires continuous learning and participation in knowledge-sharing within the team. Regular code reviews and experimentation with different architectures can help overcome technical obstacles and improve model performance.

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

AspectDeep Learning AiMachine Learning Engineer
Required CredentialsDegree in Computer Science, Data Science, or related fields; knowledge of neural networksDegree in Computer Science, Data Science, or related fields; programming skills in Python, R
Work EnvironmentResearch labs, AI development teams, tech companies focusing on AI modelsSoftware development teams, data analysis projects across various industries
Industry UsagePrimarily in AI research, autonomous systems, NLP, computer visionAcross industries for predictive modeling, data analysis, automation

Deep Learning Ai specialists focus on designing and implementing neural network models for complex AI tasks, often requiring advanced knowledge of deep neural networks. Machine Learning Engineers develop broader machine learning models, including traditional algorithms. While both roles require similar educational backgrounds, Deep Learning Ai roles are more specialized in neural networks and AI research, whereas Machine Learning Engineers work across a wider range of algorithms and applications.

What are popular job titles related to Deep Learning Ai jobs in Spring, TX?

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What job categories do people searching Deep Learning Ai jobs in Spring, TX look for?

The top searched job categories for Deep Learning Ai jobs in Spring, TX are:

What cities near Spring, TX are hiring for Deep Learning Ai jobs?

Cities near Spring, TX with the most Deep Learning Ai job openings:

Deep Learning Ai jobs near you

Intern, Deep Learning Engineer

Bot Auto

Houston, TX

$14.25 - $19/hr

Full-time, Internship

Re-posted 20 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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