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

Senior Machine Learning Engineer

Brooklyn, NY ยท On-site +1

$130K - $200K/yr

We're remote but have an office in Brooklyn, New York. We are looking for a machine learning engineer to design, build, experiment and optimize Shaped's AI discovery engine. You will be a founding ...

Senior Machine Learning Engineer

New York, NY ยท Remote

$170K - $190K/yr

We are seeking a Senior Machine Learning Engineer to join our AI & ML team in New York City. You ... This role requires deep hands-on experience with MLOps principles, cloud infrastructure, and a ...

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.

Gain America is recruiting a AI / ML Engineer for contract and contract-to-hire engagements with ... Deep-learning frameworks such as PyTorch * Experience deploying and monitoring models in production ...

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 ...

Senior Machine Learning Engineer (Remote)

New York, NY ยท 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 ...

We're looking for a senior-level Machine Learning Engineer who can move quickly while maintaining ... We're remote-first, flexible, and distributed across North and South America, bringing together ...

Showing results 21-40

Remote Deep Learning Engineer information

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

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.

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 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 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:

What job categories do people searching Remote Deep Learning Engineer jobs in New York look for?

The top searched job categories for Remote Deep Learning Engineer jobs in New York are:

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:

Artificial Intelligence Software Engineer

10xTalents

Manhattan, NY โ€ข On-site, Remote

Full-time

This job post hasย expired 1 day ago.ย Applications are no longer accepted.


Job description

Artificial Intelligence Software Engineer About the job Artificial Intelligence Software Engineer Artificial Intelligence Software Engineer - Hybrid/Remote - NYC No visa sponsorship available at this time. Our client an up and coming innovative AI start-up is seeking a highly skilled and innovative Artificial Intelligence Software Engineer to join their dynamic team. In this role, you will be responsible for designing, developing, and implementing AI algorithms and software solutions to solve complex problems across various domains. The ideal candidate will have a strong background in machine learning, deep learning, and software development, with a passion for pushing the boundaries of AI technology.

Responsibilities:

Collaborate with cross-functional teams to understand project requirements and develop AI-driven solutions tailored to specific applications. Design and implement machine learning algorithms and models for tasks such as classification, regression, clustering, and natural language processing.

Develop and optimize neural networks and deep learning architectures for tasks such as image recognition, speech recognition, and recommendation systems. Collect, preprocess, and analyze large datasets to train and evaluate AI models, ensuring robust performance and generalization. Implement scalable and efficient software solutions for deploying AI models in production environments, including cloud-based platforms and edge devices.

Collaborate with software engineers to integrate AI capabilities into existing software systems and develop AI-driven features and products. Research and evaluate emerging technologies and techniques in machine learning and AI, staying abreast of advancements in algorithms, frameworks, and tools. Document design specifications, implementation details, and best practices for internal and external stakeholders. Provide technical guidance and mentorship to junior engineers and contribute to the overall technical expertise of the team.

Requirements:

Bachelor's degree in Computer Science, Electrical Engineering, or related field. Master's or Ph.D. preferred. Proven experience in machine learning, deep learning, and AI software development, with a minimum of 3 years in a relevant role. Proficiency in programming languages such as Python, Java, or C++, and experience with AI frameworks such as TensorFlow, PyTorch, or scikit-learn. Strong understanding of machine learning algorithms, including supervised learning, unsupervised learning, and reinforcement learning.

Experience with deep learning techniques and architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models. Familiarity with software engineering best practices, including version control, testing, and code review. Excellent problem-solving skills and the ability to analyze complex technical challenges and propose innovative solutions.

Strong communication and collaboration skills, with the ability to work effectively in a multidisciplinary team environment. Experience with cloud computing platforms (e.g., AWS, Azure, Google Cloud) and containerization technologies (e.g., Docker, Kubernetes) is a plus.