ZipRecruiter

Log In

2

Remote Deep Learning Engineer Jobs in Sewell, NJ

Varsity Tutors

Structural Engineering Tutor

Chester, PA · Remote

$18 - $40/hr

About the Job The Varsity Tutors Live Learning Platform has thousands of students looking for ... Deep knowledge of structural analysis methods, reinforced concrete design, steel design, timber ...

YO AI Labs

SolidWorks Expert - Remote

Philadelphia, PA · Remote

$40 - $130/hr

Remote Job Overview We are seeking experienced SolidWorks Specialists to contribute their expertise ... Translate engineering requirements into structured CAD data suitable for AI learning and validation.

BAI

Advanced Software Engineer

Philadelphia, PA · On-site +1

$85K - $115K/yr

Our industry-leading offerings include peer sharing events, thought leadership, learning and ... off, hybrid and remote working models, tuition assistance and the ability to work in a ...

Varsity Tutors

Replit Tutor

Chester, PA · Remote

$18 - $40/hr

Deep knowledge of Replit cloud development environment including online IDE features, multi ... Emphasizes accessible development without local setup and connects Replit to learning programming ...

Xactus

AI Engineer

Broomall, PA · On-site +1

... Neptune, Deep learning (CNNs, RNNs, transformers), NLP (tokenization, embeddings, text ... The position may be remote from main offices. * May require flexibility in hours. PHYSICAL DEMANDS:

previous page

Showing results 41-60

All JobsDeep Learning Engineer JobsRemote Deep Learning Engineer JobsRemote Deep Learning Engineer Jobs in Sewell, NJ

Remote Deep Learning Engineer information

See Sewell, NJ salary details

$10.4K

$79.3K

$132.4K

How much do remote deep learning engineer jobs pay per year?

As of Aug 17, 2026, the average yearly pay for remote deep learning engineer in Sewell, NJ is $79,333.00, according to ZipRecruiter salary data. Most workers in this role earn between $68,100.00 and $131,500.00 per year, depending on experience, location, and employer.

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?

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 cities near Sewell, NJ are hiring for Remote Deep Learning Engineer jobs?

Cities near Sewell, NJ with the most Remote Deep Learning Engineer job openings:

Remote Deep Learning Engineer jobs near you

Engineer 6, Machine Learning, Data & AI

Comcast

Philadelphia, PA • On-site, Remote

$115K - $138K/yr

Full-time

Posted 5 days ago

Job description

Make your mark at Comcast -- a Fortune 30 global media and technology company. From the connectivity and platforms we provide, to the content and experiences we create, we reach hundreds of millions of customers, viewers, and guests worldwide. Become part of our award-winning technology team that turns big ideas into cutting-edge products, platforms, and solutions that our customers love. We create space to innovate, and we recognize, reward, and invest in your ideas, while ensuring you can proudly bring your authentic self to the workplace. Join us. You'll do the best work of your career right here at Comcast. (In most cases, Comcast prefers to have employees on-site collaborating unless the team has been designated as virtual due to the nature of their work. If a position is listed with both office locations and virtual offerings, Comcast may be willing to consider candidates who live greater than 100 miles from the office for the remote option.)

Job Summary

We are seeking a visionary Senior Technologist, Data & Agentic AI Enablement to shape the future of our enterprise data ecosystem and accelerate the next generation of AI-driven experiences. This enterprise leadership role is responsible for two complementary missions:
Preparing enterprise data for an Agentic AI future, ensuring data is trusted, discoverable, semantically rich, governed, and consumable by AI agents. Transforming data engineering through Agentic AI, embedding AI across the data engineering lifecycle to improve productivity, quality, reliability, and speed of delivery. The successful candidate will define the architecture, standards, and engineering patterns that enable AI to become an active participant in the design, development, testing, operation, and optimization of enterprise data platforms. This highly influential role partners closely with Data Engineering, Data Platforms, Enterprise Architecture, Security, Product, and AI teams to accelerate data modernization and AI-enabled business transformation.

Job Description

Agentic AI Transformation of Data Engineering:

Lead the strategy for integrating Agentic AI across the enterprise data engineering lifecycle, enabling AI-assisted development, operations, and platform management.

Responsibilities include:

  • Define the enterprise roadmap for incorporating AI agents into data engineering workflows, platform operations, and software delivery.
  • Partner with platform engineering teams to integrate AI capabilities into CI/CD, Infrastructure-as-Code (IaC), and DevSecOps practices.
  • Evaluate emerging agent frameworks, copilots, and autonomous engineering platforms for enterprise adoption.
  • Establish best practices and governance for AI-assisted engineering and operational processes.
Enterprise Data Strategy for AI & Agentic Systems:

Develop and drive the enterprise strategy for preparing data assets to support AI, Generative AI, and Agentic AI use cases.

Responsibilities include:

  • Define principles and reference architectures that enable AI agents to discover, access, understand, and act upon enterprise data safely and effectively.
  • Partner with business and technology leaders to identify high-value opportunities for Agentic AI solutions.
  • Establish enterprise standards that align data, AI, and business strategies.
Data Architecture & AI Readiness

Lead architectural efforts to ensure enterprise data is optimized for both human and AI consumption.

Responsibilities include:

  • Establish standards that ensure data is:
    • Discoverable
    • Well-described and semantically rich
    • Governed and trusted
    • Accessible through standardized interfaces
    • Consumable by both people and AI agents
  • Drive adoption of metadata-driven architectures, semantic models, knowledge graphs, and business ontologies.
  • Ensure enterprise data products support machine-to-machine interactions in addition to traditional analytics use cases.
Agentic Data Enablement:

Define the frameworks that enable AI agents to effectively interact with enterprise data and knowledge assets.

Responsibilities include:

  • Establish standards for exposing enterprise data through APIs, semantic layers, data products, and retrieval systems.
  • Partner with platform teams to develop capabilities supporting:
    • Retrieval-Augmented Generation (RAG)
    • Agent orchestration platforms
    • Tool and API discovery
    • Vector-based retrieval architectures
    • Context management and memory frameworks
  • Develop patterns that allow AI agents to access enterprise knowledge securely and responsibly.
Data Governance & Trust:

Ensure governance and trust frameworks evolve to support autonomous and AI-assisted decision making.

Responsibilities include:

  • Establish controls for data lineage, provenance, quality, explainability, and auditability.
  • Partner with Security, Privacy, and Risk teams to implement responsible AI controls and secure data access practices.
  • Define trust frameworks that enable AI agents to operate within approved business guardrails.
Semantic Layer & Knowledge Management:

Drive the development of enterprise semantic capabilities that improve data accessibility and AI reasoning.

Responsibilities include:

  • Lead development of enterprise semantic models and shared business definitions.
  • Improve metadata quality, business context, and knowledge accessibility across the organization.
  • Advance enterprise knowledge management practices that enhance AI reasoning, discovery, and decision support.
Platform & Ecosystem Alignment:

Collaborate across teams to ensure enterprise platforms support AI-native consumption patterns.

Responsibilities include:

  • Partner with Data Platform, Engineering, Analytics, and Product teams to align technology roadmaps.
  • Collaborate with BI and analytics teams to maintain consistent business metrics and semantic definitions across human and AI consumers.
  • Influence technology investments that enable future AI and Agentic AI capabilities.
Innovation & Thought Leadership

Serve as a strategic thought leader on AI readiness, data modernization, and enterprise architecture.

Responsibilities include:

  • Monitor emerging trends in AI, Agentic Systems, Data Architecture, and Knowledge Management.
  • Evaluate innovative technologies and identify strategic adoption opportunities.
  • Advise executive leadership on enterprise AI strategy and future-state architecture.
Qualifications
  • + years of experience in Data Architecture, Data Engineering, Enterprise Architecture, or related technology disciplines.
  • Proven experience designing and scaling enterprise data ecosystems.
  • Experience supporting AI, machine learning, advanced analytics, or data modernization initiatives.
  • Demonstrated success influencing outcomes across large, matrixed organizations.
Technical Expertise
  • Deep understanding of modern data architectures, including data warehouses, data lakes, lakehouses, and data mesh concepts.
  • Expertise in metadata management, semantic modeling, data governance, and data product design.
  • Strong understanding of APIs, event-driven architectures, and interoperability standards.
  • Experience with AI technologies, including LLMs, RAG architectures, vector databases, agent frameworks, and AI orchestration platforms.
  • Knowledge of modern software engineering, DevSecOps, CI/CD, and cloud-native architectures.
Leadership & Communication
  • Strong executive communication and stakeholder management skills.
  • Ability to translate emerging technologies into practical enterprise strategies.
  • Proven ability to lead through influence across business and technology organizations.
  • Demonstrated thought leadership in data, AI, or enterprise architecture domains.
Preferred Candidate Profile :

A strategic technology leader with deep expertise in enterprise data architecture and a passion for advancing AI adoption. This individual combines strong technical vision, architectural leadership, and business acumen to help the organization build a trusted, AI-ready data foundation while transforming the way engineering teams work through Agentic AI.

Disclaimer:

This information has been designed to indicate the general nature and level of work performed by employees in this role. It is not designed to contain or be interpreted as a comprehensive inventory of all duties, responsibilities and qualifications.

Skills

Agentic AI, AI Adoption, Data Architecture Development, Data Engineering, Data Strategies, Enterprise Data

Compensation

This job can be performed in Virginia, and District of Columbia with a Pay Range of $224,190.44 - $351,571.37Comcast intends to offer the selected candidate base pay within this range, dependent on job-related, non-discriminatory factors such as experience. The application window is 30 days from the date job is posted, unless the number of applicants requires it to close sooner or later.

Base pay is one part of the Total Rewards that Comcast provides to compensate and recognize employees for their work. Most sales positions are eligible for a Commission under the terms of an applicable plan, while most non-sales positions are eligible for a Bonus. Additionally, Comcast provides best-in-class Benefits to eligible employees. We believe that benefits should connect you to the support you need when it matters most, and should help you care for those who matter most. That's why we provide an array of options, expert guidance and always-on tools, that are personalized to meet the needs of your reality - to help support you physically, financially and emotionally through the big milestones and in your everyday life. Please visit the compensation and benefits summary on our careers site for more details.

Education

Bachelor's DegreeWhile possessing the stated degree is preferred, Comcast also may consider applicants who hold some combination of coursework and experience, or who have extensive related professional experience.

Certifications (if applicable)

Relevant Work Experience

15 Years +Comcast is an equal opportunity workplace. We will consider all qualified applicants for employment without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability, veteran status, genetic information, or any other basis protected by applicable law.

Apply