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Remote Deep Learning Engineer Jobs in New York (NOW HIRING)

1 point system

GenAI Engineer

New York, NY · Remote

Required Skills - AI, Deep Learning Frameworks, Python Build and develop models to improve and ... Programming Skills: Expertise in Python and tools like Hugging Face, Langchain, and OpenAI API.

Mercor

Machine Learning Engineer

New York, NY · Remote

$70 - $100/hr

Remote Commitment: 40 hours/week Role Responsibilities * Guide research and engineering teams to ... Learning , Data Science , Software Engineering , Computer Science , Statistics , Biology ...

Apply Early

Reddit

Machine Learning Engineer

New York, NY · On-site +1

$223K - $260K/yr

... deep neural networks within high dimensional information system. Work on large-scale data systems ... engineering, research and marketing. Train, evaluate, test, and deploy machine learning models.

Adidev Technologies Inc

AI/ML Developer

Manhattan, NY · Remote

Adidev is looking for an adept Machine Learning Engineer to take the helm in deploying advanced ... Hands-on experience with Deep Learning, LLM, Python, TensorFlow, PyTorch and other AI frameworks ...

Zencastr

Senior Machine Learning Engineer (Remote)

New York, NY · On-site +1

$114K - $157K/yr

We are looking for an outstanding machine learning engineer to join our team! The role will provide an opportunity to work on large scale machine learning to improve the podcast creation experience ...

Mercor

Senior Machine Learning Engineer

New York, NY · Remote

$70 - $100/hr

Remote Commitment: 40 hours/week Role Responsibilities * Guide research and engineering teams to ... Learning , Data Science , Software Engineering , Computer Science , Statistics , Biology ...

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Remote Deep Learning Engineer information

How do Remote Deep Learning Engineers typically collaborate with cross-functional teams despite working remotely?

Remote Deep Learning Engineers frequently collaborate with data scientists, product managers, and software engineers using digital tools such as Slack, Zoom, and collaborative code platforms like GitHub. Regular virtual meetings and sprint planning sessions help ensure alignment on project goals and milestones. Clear documentation and asynchronous communication are crucial for effective teamwork, especially when team members are in different time zones. This collaborative structure enables remote engineers to contribute meaningfully to model development, deployment, and integration while maintaining flexibility.

What are the key skills and qualifications needed to thrive as a Remote Deep Learning Engineer, and why are they important?

To thrive as a Remote Deep Learning Engineer, you need a strong background in machine learning, deep learning frameworks, and programming languages like Python, usually supported by a degree in computer science or a related field. Familiarity with tools such as TensorFlow, PyTorch, cloud platforms (e.g., AWS, GCP), and version control systems is typically required, with certifications in AI or cloud technologies being advantageous. Excellent problem-solving, communication, and self-management skills make candidates stand out in remote environments. These skills and qualities are essential for developing effective AI solutions, collaborating across distributed teams, and driving innovation in the fast-evolving field of deep learning.

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

AspectRemote Deep Learning EngineerRemote Machine Learning Engineer
Required CredentialsBachelor's/Master's in CS, AI, or related; experience with deep learning frameworksBachelor's/Master's in CS, Data Science, or related; experience with ML algorithms
Work EnvironmentResearch and development, model training, neural network designData analysis, model deployment, algorithm development
Employer & Industry UsageTech companies, AI startups, research institutionsTech firms, finance, healthcare, e-commerce

Remote Deep Learning Engineers focus on designing and training neural networks for complex AI tasks, while Remote Machine Learning Engineers work on broader ML models and algorithms. Both roles require strong programming skills and knowledge of machine learning frameworks, but Deep Learning Engineers specialize in neural networks and large-scale data processing.

What is a Remote Deep Learning Engineer?

A Remote Deep Learning Engineer is a professional who works primarily online to design, develop, and implement deep learning models and algorithms. These engineers use neural networks and large datasets to solve complex problems in fields like computer vision, natural language processing, and more. Working remotely, they collaborate with team members via digital tools, write code, optimize models, and often deploy solutions to cloud environments. This role requires strong programming skills, experience with deep learning frameworks (like TensorFlow or PyTorch), and the ability to work independently in a distributed team setting.
What are the most commonly searched types of Deep Learning Engineer jobs in New York? The most popular types of Deep Learning Engineer jobs in New York are:
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What cities in New York are hiring for Remote Deep Learning Engineer jobs? Cities in New York with the most Remote Deep Learning Engineer job openings:
Remote Deep Learning Engineer jobs near you
GenAI Engineer

GenAI Engineer

1 point system

New York, NY • Remote

Contractor

Posted 11 days ago

Job description

Self-motivated individual with a passion for solving hard problems using AI/GenAI.

Must have collaborative and an innovative mindset.

Required Skills -    AI, Deep Learning Frameworks, Python

Build and develop models to improve and enhance the current environment for better understanding and recommendations.

Skill sets: Ability to develop prototypes, PoCs, MVPs using one or more of the following,

AI/ML Knowledge: Strong foundation in AI, deep learning, and machine learning principles.

Programming Skills: Expertise in Python and tools like Hugging Face, Langchain, and OpenAI API.

Deep Learning Frameworks:

Experience with TensorFlow, Keras, and PyTorch.

Cloud Platforms: Familiar with Google Model Garden, Amazon Bedrock, and Nvidia Nim.

Multi-Modal Data and AI Tools: Experience with multi-modal data and intelligent agent-based tools.