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Remote Deep Learning Jobs in Phoenix, AZ (NOW HIRING)

GearUp2Success

Media & Lifestyle Marketing

Phoenix, AZ · Remote

Remote | Performance-based | Self-Employed We're seeking individuals who are ready to rise into ... You value growth, self-awareness, and learning, approaching life with both confidence and humility.

GearUp2Success

Media & Lifestyle Marketing

Phoenix, AZ · On-site +1

Remote Performance-based Self-Employed We're seeking individuals who are ready to rise into their ... You value growth, self-awareness, and learning, approaching life with both confidence and humility.

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All JobsDeep Learning JobsRemote Deep Learning JobsRemote Deep Learning Jobs in Phoenix, AZ

Remote Deep Learning information

See Phoenix, AZ salary details

$10.9K

$83.3K

$139K

How much do remote deep learning jobs pay per year?

As of Aug 20, 2026, the average yearly pay for remote deep learning in Phoenix, AZ is $83,291.00, according to ZipRecruiter salary data. Most workers in this role earn between $71,500.00 and $138,000.00 per year, depending on experience, location, and employer.

What is a remote deep learning engineer?

A Remote Deep Learning job involves working with artificial intelligence and machine learning models, particularly using deep neural networks, from a location outside a traditional office, often from home. Professionals in this field design, build, and optimize algorithms that enable computers to learn from large amounts of data. They often work on projects such as image and speech recognition, natural language processing, or autonomous systems. The remote aspect allows flexibility and access to global opportunities, but requires strong communication skills and the ability to collaborate virtually with teams.

What skills and qualifications are needed to thrive as a remote deep learning engineer?

To thrive as a Remote Deep Learning Engineer, you need strong programming skills in Python, a deep understanding of machine learning algorithms, and typically a degree in computer science, engineering, or a related field. Proficiency with frameworks like TensorFlow or PyTorch, as well as cloud computing platforms such as AWS or Google Cloud, is essential, and certifications in these technologies can be advantageous. Excellent problem-solving abilities, self-motivation, and clear communication are crucial soft skills for remote collaboration and project delivery. These skills ensure effective development, deployment, and maintenance of deep learning models while working independently in distributed teams.

What are common challenges faced by remote deep learning engineers, and how can they be addressed?

Remote deep learning engineers often encounter challenges such as limited access to high-performance computing resources, communication barriers with distributed teams, and difficulties in collaborating on large codebases or datasets. These issues can be mitigated by leveraging cloud-based platforms for scalable computing, using clear communication tools like Slack or Zoom for regular check-ins, and employing version control systems like Git for collaborative code management. Proactively setting up workflows and documentation helps ensure smooth collaboration and project continuity within a remote environment.

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

AspectRemote Deep LearningRemote Machine Learning Engineer
Required CredentialsBachelor's/Master's in CS, AI, or related; experience with neural networksBachelor's/Master's in CS, Data Science, or related; experience with algorithms and data modeling
Work EnvironmentCollaborative teams, research-focused, often in tech or AI companiesDevelopment teams, data-driven projects, across various industries
Employer & Industry UsageTech firms, AI startups, research institutionsTech companies, finance, healthcare, e-commerce

Remote Deep Learning specialists focus on designing and training neural networks for AI applications, often requiring advanced knowledge of deep neural architectures. Remote Machine Learning Engineers work on developing algorithms and models for broader data analysis and predictive tasks. While both roles involve machine learning, deep learning emphasizes neural networks, whereas machine learning engineers may work with a variety of algorithms across industries.

What are popular job titles related to Remote Deep Learning jobs in Phoenix, AZ?

For Remote Deep Learning jobs in Phoenix, AZ, the most frequently searched job titles are:

What job categories do people searching Remote Deep Learning jobs in Phoenix, AZ look for?

The top searched job categories for Remote Deep Learning jobs in Phoenix, AZ are:

What cities near Phoenix, AZ are hiring for Remote Deep Learning jobs?

Cities near Phoenix, AZ with the most Remote Deep Learning job openings:

Remote Deep Learning jobs near you

Senior AI Engineer / Data Scientist

Koantek

Chandler, AZ • Remote

Contractor

Re-posted 29 days ago

Job description

Senior AI Engineer / Data Scientist (Consulting) Location: United States (Remote) Employment Type: Full-Time / Contract Experience Level: Senior About the Role: We are seeking an experienced, highly technical Senior AI Engineer / Data Scientist to join our customer-facing consulting team. This remote role requires a unique blend of advanced Machine Learning (ML) expertise, deep knowledge of MLOps principles, and a proven track record in client-facing implementation. You will design, deploy, and maintain production-grade ML solutions, including advanced Generative AI and NLP models, for our diverse client base.

Key Responsibilities: * Technical Consulting: Lead end-to-end ML implementations directly with clients, translating business problems into robust technical solutions. * MLOps and Pipelines: Design, build, and maintain production-grade ML pipelines with a strong focus on CI/CD, automation, and scalability. * GenAI and NLP Deployment: Implement and optimize cutting-edge Generative AI applications (such as LLMs and RAG) in live production settings.

* Infrastructure and Data Scale: Manage underlying infrastructure using Docker, pipeline orchestrators, and distributed computing frameworks like Apache Spark. * Stakeholder Management: Clearly communicate technical findings, proposals, and project status to both technical and non-technical audiences. Required Qualifications: * 4+ years of professional experience developing, deploying, and maintaining ML models in a live production environment (Mandatory).

* 3+ years of experience in a customer-facing consulting or Solutions Architect role. * Strong expertise in the MLOps lifecycle (model versioning, testing, monitoring, and automated deployment). * Solid hands-on experience with containerization (Docker) and data pipeline orchestration.

* Proven track record of deploying Generative AI and NLP solutions for client applications. * Excellent verbal and written communication skills. Preferred Qualifications: * Hands-on experience with modern ML platform stacks, specifically Databricks MLOps Stacks.

* Deep knowledge of large-scale data processing and distributed machine learning techniques. * A strong commitment to continuous learning in emerging ML fields and GenAI application architectures.

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