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Deep Learning Ai Jobs in Jackson, MS (NOW HIRING)

Apex Systems

AI Security Lead

Ridgeland, MS · On-site

$105 - $175/hr

Deep expertise in cloud architecture, distributed systems, and scalable frameworks. * Advanced ... learning. We offer quality career resources, training, certifications, development opportunities ...

Apex Systems

AI Security Lead

Ridgeland, MS · On-site

$175K/yr

Deep expertise in cloud architecture, distributed systems, and scalable frameworks. * Advanced ... learning. We offer quality career resources, training, certifications, development opportunities ...

Ryder System, Inc.

Senior Director AI Security

Jackson, MS · On-site

$150 - $200/hr

... learning platforms, autonomous systems, and emerging AI capabilities. This role partners closely ... Deep understanding of LLMs, generative AI, and model lifecycle., Required * Strong knowledge of ...

Apex Systems

Gen AI Engineer Lead

Ridgeland, MS · On-site

$87K - $115K/yr

This role is ideal for a technical leader with deep expertise in Generative AI, agent-based systems ... learning from human feedback (RLHF) or synthetic data augmentation. Establish model-drift detection ...

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Deep Learning Ai information

See Jackson, MS salary details

$9.6K

$73.1K

$122K

How much do deep learning ai jobs pay per year?

As of Sep 5, 2026, the average yearly pay for deep learning ai in Jackson, MS is $73,100.00, according to ZipRecruiter salary data. Most workers in this role earn between $62,700.00 and $121,100.00 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.

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Deep Learning Ai jobs near you

Research Scientist Intern (2025)

Whiterabbit.ai

Jackson, MS • On-site

Other

Re-posted 16 days ago

Job description

We are looking for a Research Scientist Intern to push the state of the art of our AI models. As a Research Scientist Intern at Whiterabbit.ai, you will:

  • Play a key role in architecting the algorithms and models that will power our products
  • Train on a dedicated high-performance compute cluster specialized for deep learning research
  • Work with doctors and healthcare professionals to identify serious problems and leverage their domain expertise to build robust solutions
  • Remain an active contributor to the research community by partnering with universities and publishing high impact papers

Who we are:

Our mission at Whiterabbit.ai is to save lives and eliminate suffering through the early detection of cancer with artificial intelligence. We collaborate closely with one of the top medical schools in the country and have exclusive access to one of the world’s largest cancer datasets with millions of images. We invent algorithms that make doctors more productive, more accurate, and more capable. We build products and services with a relentless focus on transforming the patient’s healthcare experience.

Responsibilities

  • Develop highly scalable classifiers and detectors that solve real-world problems
  • Learn and understand a large body of research in deep learning and machine learning
  • Participate in cutting-edge research for medical applications of computer vision

Must Have Experience

  • Experience with deep learning and convolutional networks
  • Strong theoretical and empirical research background
  • Fluency with a deep learning framework and Python

Nice to Have Experience

  • Contributions to research communities and efforts, such as publications at conferences like CVPR, NeurIPS, ICCV, ECCV, ICML, and ICLR
  • Large scale machine learning experience working with terabytes of data
  • Implemented custom operations/modules in a deep learning framework
  • Imagination, ambition, and curiosity

Apply