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Deep Learning Ai Jobs in Carson, CA (NOW HIRING)

Steg.AI

Machine Learning Scientist

Irvine, CA · On-site

$140 - $200/hr

... AI technology for watermarking, steganography, or media provenance.## You will work with the machine learning team to design and implement state of the art deep learning algorithms for steganography ...

New

Masimo

Sr Engineer, AI/Machine Learning

Irvine, CA · On-site

$140K - $170K/yr

Design and develop AI and ML models to solve complex problems ... Develop new advanced algorithms using, machine learning techniques, deep learning models, digital ...

Masimo

Sr Engineer, AI/Machine Learning

Irvine, CA · On-site

$140K - $170K/yr

Design and develop AI and ML models to solve complex problems ... Develop new advanced algorithms using, machine learning techniques, deep learning models, digital ...

Danta Technologies

Gen AI Engineer

Los Angeles, CA · On-site

Key Skills - Agentic AI, Gen AI, AI/ML, Data Science, SQL, Python, Pandas, Deep Learning, Machine Learning, LLM, Data Structures, Bert Transformers, NLP, PyTorch, PySpark. The ideal candidate will ...

Voxelcloud

Machine Learning Engineer

Los Angeles, CA · On-site

Company Description Founded in 2016, VoxelCloud, Inc. is a Los Angeles-based worldwide leader in AI ... Develop deep learning models for prototyping and production purposes according to product feature ...

Voxelcloud

Machine Learning Engineer

Los Angeles, CA · On-site

Company Description Founded in 2016, VoxelCloud, Inc. is a Los Angeles-based worldwide leader in AI ... Develop deep learning models for prototyping and production purposes according to product feature ...

Hadrian

Machine Learning Engineer

Los Angeles, CA · On-site

... deep learning models • You have a passion for manufacturing and believe that the industry needs better software • Prior experience working in a startup environment Company : Hadrian builds AI ...

Staffingine LLC

AI/ML Engineer

Los Angeles, CA · On-site

AI/ML Engineer Job Location: Los Angeles - California - USA Job Type: Contract * 3-5 years of ... Proven experience with one or more deep learning frameworks such as TensorFlow or PyTorch * Handson ...

Hadrian

Machine Learning Engineer

Los Angeles, CA · On-site

... deep learning models • You have a passion for manufacturing and believe that the industry needs better software • Prior experience working in a startup environment Company : Hadrian builds AI ...

FieldAI

3D Machine Learning Engineer

Irvine, CA · On-site

$150K - $200K/yr

FieldAI's Irvine team is where embodied AI meets real robots, real sensors, and real field ... Train, optimize, and deploy deep learning models using PyTorch, TensorFlow, or equivalent ...

FieldAI

3D Machine Learning Engineer

Irvine, CA

$150K - $200K/yr

FieldAI's Irvine team is where embodied AI meets real robots, real sensors, and real field ... Train, optimize, and deploy deep learning models using PyTorch, TensorFlow, or equivalent ...

FieldAI

3D Machine Learning Engineer

Irvine, CA

$150K - $200K/yr

FieldAI's Irvine team is where embodied AI meets real robots, real sensors, and real field ... Train, optimize, and deploy deep learning models using PyTorch, TensorFlow, or equivalent ...

Warnerbros

Principal Data Scientist

Burbank, CA

... AI innovation, and to ensure delivery of impactful solutions for the business. The Daily * Develop innovative data science solutions that utilize machine learning and deep learning algorithms ...

Masimo

Engineer II, AI/Machine Learning

Irvine, CA · On-site

$120K - $150K/yr

The AI/Machine Learning Engineer II will be part of the R&D team at Masimo with focus on design and ... Experience in artificial intelligence, machine learning and/or deep learning within the medical ...

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All JobsDeep Learning Ai JobsDeep Learning Ai Jobs in Carson, CA

Deep Learning Ai information

See Carson, CA salary details

$11.5K

$87.7K

$146.4K

How much do deep learning ai jobs pay per year?

As of Aug 21, 2026, the average yearly pay for deep learning ai in Carson, CA is $87,730.00, according to ZipRecruiter salary data. Most workers in this role earn between $75,300.00 and $145,400.00 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 Carson, CA?

For Deep Learning Ai jobs in Carson, CA, the most frequently searched job titles are:

What job categories do people searching Deep Learning Ai jobs in Carson, CA look for?

The top searched job categories for Deep Learning Ai jobs in Carson, CA are:

What cities near Carson, CA are hiring for Deep Learning Ai jobs?

Cities near Carson, CA with the most Deep Learning Ai job openings:

Deep Learning Ai jobs near you
Infographic showing various Deep Learning Ai job openings in Carson, CA as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 24% Part Time, 1% Contract, and 1% Nights. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $87,730 per year, or $42.2 per hour.

AI Research Assistant - Machine Learning & AI

StudyFetch

Beverly Hills, CA • On-site, Remote

$35 - $50/hr

Part-time

Posted 3 days ago

New

Job description

About Studyfetch
StudyFetch is the #1 AI-native learning platform globally, transforming how millions of students learn through personalized AI-powered education. We're growing fast with backing from top-tier investors and a mission that's redefining the future of education and ethical learning.
About the Role
We are looking for a highly motivated Master's student in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related technical field to join our AI research team on a part-time basis.
Our research focuses primarily on the intersection of artificial intelligence and education, including the development and evaluation of AI systems that can improve teaching and learning. You will work directly with researchers and engineers on projects involving machine learning, generative AI, large language models, multimodal AI, educational data, model evaluation, and AI systems.
This is a hands-on research position. You will not simply be assisting with administrative research tasks-you will be expected to read papers, implement ideas, run experiments, analyze results, and contribute to the development of new research directions.
Depending on your interests and experience, your work may include developing and evaluating models, building research datasets, designing benchmarks and evaluation methodologies, reproducing published research, fine-tuning open-source models, analyzing educational data, and investigating novel applications of AI in education.
You will also contribute to the broader research development process, including identifying relevant research opportunities, analyzing RFPs and funding opportunities, and assisting with grant and research proposal development.
What You'll Do
  • Read and analyze recent research papers in machine learning, AI, and AI in education.
  • Implement and reproduce methods from recent research.
  • Design and conduct controlled experiments and ablation studies.
  • Train, fine-tune, and evaluate machine learning models.
  • Develop datasets and data-processing pipelines for AI research.
  • Build and maintain evaluation and benchmarking systems.
  • Analyze model performance and experimental results.
  • Investigate new approaches to improving AI capabilities, reliability, and effectiveness in educational settings.
  • Develop research prototypes in Python and modern ML frameworks.
  • Analyze educational datasets and student interaction data to identify research opportunities and patterns.
  • Document experiments, findings, and methodologies.
  • Collaborate with researchers and engineers to refine research hypotheses.
  • Contribute to technical reports, research papers, presentations, and potentially open-source projects.
  • Research and analyze RFPs, grant opportunities, and government or foundation funding programs relevant to AI and education.
  • Assist with the development of grant proposals, research proposals, technical narratives, and supporting materials.
  • Help identify research questions and proposed technical approaches that align with funding opportunities.
  • Track relevant developments in AI research, education technology, and government research priorities.

Areas of Research
Our research primarily focuses on the application and development of AI for education. Projects may span several areas, including:
  • Large Language Models (LLMs)
  • Generative AI
  • Multimodal AI
  • Computer vision
  • Natural language processing
  • AI tutoring and educational agents
  • Personalized learning
  • Student modeling and learning analytics
  • Model training and fine-tuning
  • Reinforcement learning and post-training
  • AI evaluation and benchmarking
  • Synthetic data generation
  • Representation learning
  • Retrieval-augmented generation (RAG)
  • Educational datasets and data infrastructure
  • AI safety, reliability, and evaluation in education

You do not need experience in all of these areas. Depth in one area, strong research fundamentals, and demonstrated ability to learn quickly are more important than breadth.
Required Qualifications
  • Currently pursuing or recently completed a Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Mathematics, Statistics, Electrical Engineering, or a closely related field.
  • Strong foundation in machine learning and deep learning.
  • Strong Python programming skills.
  • Experience with at least one modern ML framework, preferably PyTorch.
  • Familiarity with fundamental concepts in neural networks, optimization, and statistical learning.
  • Ability to read and understand technical research papers.
  • Experience conducting technical or academic projects involving machine learning.
  • Strong analytical and problem-solving skills.
  • Strong written communication skills and the ability to clearly explain technical concepts.
  • Ability to work independently while collaborating closely with a research team.
  • Genuine interest in the application of AI to education.
Preferred Qualifications
  • Experience with LLMs, Transformers, multimodal models, computer vision, NLP, reinforcement learning, or generative AI.
  • Experience fine-tuning or training machine learning models.
  • Experience working with GPU-based ML environments.
  • Familiarity with Hugging Face Transformers or similar ML ecosystems.
  • Experience designing datasets, benchmarks, or model evaluations.
  • Experience reproducing results from published research.
  • Research experience through a Master's thesis, research lab, internship, or independent project.
  • Experience working with educational, student, or learning data.
  • Experience with AI evaluation, benchmarking, or experimental design.
  • Publications, preprints, conference submissions, or other demonstrated research output.
  • Contributions to open-source ML/AI projects.
  • Familiarity with Linux, Git, Docker, or distributed computing.
  • Experience researching or writing grant proposals, RFP responses, technical proposals, or research funding applications.
  • Familiarity with federal, state, foundation, or other research funding programs.

What We're Looking For
We're particularly interested in people who are curious, technically rigorous, and excited by the process of discovering something that doesn't already have an obvious answer.
The strongest candidates will be able to demonstrate that they have gone beyond simply completing coursework-for example, by:
  • Building and training their own models.
  • Reproducing a research paper.
  • Conducting an independent ML research project.
  • Developing a substantial Master's thesis.
  • Creating an interesting dataset or benchmark.
  • Investigating why a model succeeds or fails.
  • Working with real-world educational or student data.
  • Publishing or presenting research.
  • Contributing to an open-source ML project.
  • Writing or contributing to a research proposal or grant application.

You do not need to have published a paper to be successful in this role. We care more about your ability to think scientifically, write good code, design meaningful experiments, communicate clearly, and learn quickly.
We also value candidates who are interested in the broader research process-not just model development-including identifying research opportunities, understanding funding priorities, analyzing RFPs, and helping translate technical ideas into compelling research proposals.
Position Details
  • Position: Part-Time AI Research Assistant
  • Focus: Machine Learning & AI for Education
  • Hours: Approximately 15-25 hours per week
  • Location: [Remote / Hybrid]
  • Schedule: Flexible schedule designed to accommodate graduate coursework and research commitments
  • Compensation: $35-50/hour, depending on experience and qualifications

This position is designed for a graduate student who wants meaningful, hands-on experience conducting applied AI research in an industry research environment, with opportunities to contribute to research publications, funded research initiatives, datasets, benchmarks, and real-world AI systems.
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