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Remote Deep Learning Jobs (NOW HIRING)

Sunergi Inc

$110K - $140K/yr

Previous experience with statistical modeling and deep learning frameworks / libraries we use is required. * Strong aptitude for learning new technologies related to Data Management and Data Science.

Azumo

Data Scientist, Applied AI - Remote

The position is FULLY REMOTE , based in Latin America. Professional English proficiency (B2/C1 ... If you have a passion for designing and implementing advanced machine learning and deep learning ...

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Remote Deep Learning information

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$11K

$83.9K

$140K

How much do remote deep learning jobs pay per year?

As of Sep 10, 2026, the average yearly pay for remote deep learning in the United States is $83,885.00, according to ZipRecruiter salary data. Most workers in this role earn between $72,000.00 and $139,000.00 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.

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Remote Deep Learning jobs near you
Infographic showing various Remote Deep Learning job openings in the United States as of September 2026, with employment types broken down into 67% Full Time, 11% Part Time, and 22% Contract. Highlights an 100% Remote job distribution, with an average salary of $83,885 per year, or $40.3 per hour.

Machine Learning Engineer (Remote)

Remote

Sunergi Inc
Internet and IT • 1 - 10 employees
Learn more about Sunergi Incexternal

$110K - $140K/yr

Full-time

Re-posted 12 days ago

Key responsibilities

  • Implement data pipelines focused on Machine Learning applications and develop data sets for proof of concepts.

  • Partner with cross-functional teams to define, develop, and implement data technology solutions for ML applications.

  • Take ownership of individual platform components, help set the vision and architecture, and identify requirements for new features.

Job description

Sunergi Inc.Machine Learning EngineerFull-time


We are expanding rapidly and are seeking new, experienced and hands-on team members who think outside of the box (and are not afraid to share their thoughts), will deliver unique ideas and like to work in a fast-paced environment on cutting edge projects.


Our missionWe aim to map the best plots for solar panels in the United States.



The Machine Learning Engineer role is all about building, recruiting, management, internal communication and delivery - getting the product out the door, while ensuring the team is hitting their mark. Furthermore, the role will help to grow the engineering team, establish the engineering culture and remove impediments to help team members be able to provide their best.
Key Qualifications
  • You are a core contributor on ML projects with a focus on data ingestion, transformation and presentation for ML apps and reporting.
  • Passionate and dedicated track record of designing and implementing scalable, performant data pipelines, data services, and data products.
  • This is a hands-on position, expect to write more code.
  • Proficiency in at least one programming language (Java, Python or Scala) and a tried understanding of SQL.
  • Previous experience of dealing with multivariate data at petabyte scale, especially in the time-series domain.
  • Be able to communicate collaboratively with Data Scientists and ML Software developers to understand requirements we have and deliver best in class data platform.
  • Previous experience with statistical modeling and deep learning frameworks / libraries we use is required.
  • Strong aptitude for learning new technologies related to Data Management and Data Science.
  • Proven record to create and perform independently and within a fast-paced, team-oriented environment.
  • Work with structured and unstructured data. Perform data cleansing, scraping unstructured data and converting into structured data.
  • Evaluate, benchmark and improve the scalability, robustness, efficiency and performance of big data platform and applications.
  • Experience with Kubernetes, Docker is a plus
Description

In this role, handle implementing data pipelines focused on Machine Learning applications. 


  • You will develop data sets for POCs to demonstrate new insights. 
  • Several of these may lead to fully operational ML models and deploy and own the life-cycle on in-house and third party cloud environments. 
  • You will partner with various cross functional teams to define, develop and implement data technology solutions, with an emphasis on providing superior foundational data for ML applications. 
  • A strong understanding of distributed data systems and experience in using open source frameworks to build applications is required. 
  • A solid understanding of Deep learning platforms such as Keras, Tensor-flow and/or PyTorch is highly desirable, as is an ability to deploy solutions based on these platforms. 
  • Leveraging GPU & CPU resources as appropriate / understanding capacity requirements for ML Workloads, and working with partner teams to ensure scalability, business continuity and appropriate turnaround time is a key part of the operationalization effort. 
  • As a member of the team, you will be expected to take ownership of individual platform components and help set the vision and architecture for those. 
  • In the process, you will identify the requirements of new features, and propose design and drive the solution. 
  • A strong understanding of data governance and data privacy is expected for this role in keeping with Apple's strong commitment to the same.
Education & Experience

B.S or M.S in Computer Science, Mathematics, Statistics, Operational Research, Data Science / equivalent experience.

Additional Requirements
  • 3+ years of proven experience with Kubernetes, Docker is a plus

Compensation

  • 0.25% company equity, with vesting options up to 2%.
  • A strong, competitive salary upon reaching a seed round of funding. In the range of $110k-$140k/ year.



Employment Type: FULL_TIME

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