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Remote Deep Learning information

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How much do remote deep learning jobs pay per hour?

As of Jul 20, 2026, the average hourly pay for remote deep learning in New York is $62.48, according to ZipRecruiter salary data. Most workers in this role earn between $50.58 and $77.36 per hour, depending on experience, location, and employer.

What is a Remote Deep Learning job?

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 are some 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.

How can I make $100,000 a year working from home?

A remote deep learning professional can reach a $100,000 annual income by gaining advanced skills in machine learning frameworks, building a strong portfolio, and working for companies that offer competitive salaries or freelance projects. Earning this level often requires experience, specialized knowledge, and the ability to deliver high-quality models efficiently. Certifications in deep learning and proficiency with tools like Python, TensorFlow, or PyTorch can also enhance earning potential.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as a senior machine learning engineer, AI research director, or chief AI officer, often requiring advanced skills in deep learning, data science, and programming. These roles usually involve leadership responsibilities, extensive experience, and may include stock options or bonuses as part of compensation packages.

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.

Which 3 jobs will survive AI?

In the field of remote deep learning, roles such as data scientists, machine learning engineers, and AI research scientists are likely to persist due to their reliance on complex problem-solving, domain expertise, and ongoing innovation. These jobs require advanced skills in programming, mathematics, and understanding of AI frameworks, making them less susceptible to automation by AI systems. Continuous learning and staying updated with new tools and techniques are essential for long-term career stability in this area.

How to make $1000 a week remotely?

Remote deep learning professionals can earn $1000 or more weekly by taking on freelance projects, consulting, or working for companies that pay competitive rates. Building a strong portfolio, acquiring relevant skills in Python and machine learning frameworks, and obtaining certifications can help increase earning potential. Consistent work and specialized expertise are key to reaching this income level remotely.

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 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 the most commonly searched types of Deep Learning jobs in New York? The most popular types of Deep Learning jobs in New York are:
What are popular job titles related to Remote Deep Learning jobs in New York? For Remote Deep Learning jobs in New York, the most frequently searched job titles are:
What job categories do people searching Remote Deep Learning jobs in New York look for? The top searched job categories for Remote Deep Learning jobs in New York are:
What cities in New York are hiring for Remote Deep Learning jobs? Cities in New York with the most Remote Deep Learning job openings:
Remote Deep Learning jobs near you
Infographic showing various Remote Deep Learning job openings in New York as of July 2026, with employment types broken down into 74% Full Time, 24% Part Time, and 2% Contract. Highlights an 76% Physical, 2% Hybrid, and 22% Remote job distribution, with an average salary of $129,962 per year, or $62.5 per hour.
Staff Machine Learning Scientist - Personalization (Open to Remote)

Staff Machine Learning Scientist - Personalization (Open to Remote)

Bertelsmann

Manhattan, NY • On-site, Remote

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 15 days ago

Job description

Company Description
Penguin Random House is the leading adult and children's publishing house in North America, the United Kingdom and many other regions around the world. In publishing the best books in every genre and subject for all ages, we are committed to quality, excellence in execution, and innovation throughout the entire publishing process: editorial, design, marketing, publicity, sales, production, and distribution. Our vibrant and diverse international community of nearly 300 publishing brands and imprints include Ballantine Bantam Dell, Berkley, Clarkson Potter, Crown, DK, Doubleday, Dutton, Grosset & Dunlap, Little Golden Books, Knopf, Modern Library, Pantheon, Penguin Books, Penguin Press, Penguin Random House Audio, Penguin Young Readers, Portfolio, Puffin, Putnam, Random House, Random House Children's Books, Riverhead, Ten Speed Press, Viking, and Vintage, among others. More information can be found at http://www.penguinrandomhouse.com/.
Job Description
Penguin Random House is the largest trade publishing company in the world. The Data Science team is seeking a Staff Machine Learning Scientist to lead and advance the development of personalization products, including recommender systems for our websites, email programs, and online marketing. Personalization is a core growth lever for book discovery and customer engagement, directly improving how readers find the right books across every digital touchpoint. Improving recommendation quality and relevance has a direct downstream impact on customer experience and business outcomes.
We are investing in expanding our portfolio of business-critical personalization products and further improving our existing models. This role will own personalization and recommender system work end-to-end, from model development to deployment to output monitoring, in close partnership with business stakeholders, platform engineers, and the rest of the personalization group.
We have a mature machine learning practice and strong infrastructure, supported by strong data warehouse and DevOps partners. We are transitioning to AI-accelerated development and use modern agentic coding tools like Claude Code to speed up how we build and maintain personalization systems, with rigorous quality gates including tests, reproducible workflows, and measurable improvements in model performance and reliability. Experience with Claude Code or agentic workflows is a plus, but we prioritize strong fundamentals and the ability and willingness to learn new workflows effectively.
Specific responsibilities include:
  • Define and drive the technical roadmap for personalization and recommender systems, prioritizing roadmap items to meet business goals and defining short-term vision for the team.
  • Propose and deliver R&D that directly shapes roadmaps, multiple projects, and long-term deliverables. Models are used over the long term by multiple products and teams.
  • Design and lead the development of software used by multiple teams, ensuring long-term maintainability, scalability, and adaptability.
  • Ensure complex, multi-service personalization products meet SLAs and provide correct results over time. Adapt systems to changing business needs and resolve multi-product, multi-team service incidents.
  • Establish and enforce experimentation best practices, including A/B testing frameworks, offline evaluation methodology, and metrics design across personalization surfaces.
  • Lead team meetings, ensure the team's progress on the roadmap, and make technical decisions that unblock projects.
  • Manage stakeholders' expectations with data-driven narratives and communicate effectively with senior leadership to align on strategy and track progress.
  • Drive organizational efficiency and business impact by implementing new technologies and processes. Foster a collaborative and high-performance team culture.
  • Mentor senior and mid-level scientists, setting high code quality standards and best practices for the team.
  • Stay current with advances in recommender systems, LLMs for personalization, and representation learning, bringing relevant advances into production when they deliver measurable improvement.

Qualifications
Basic qualifications:
  • PhD in Computer Science, Machine Learning, Engineering, Operations Research, Statistics, or a related quantitative field, OR Master's with 8+ years of applied ML experience.
  • Deep expertise in recommender systems, personalization, ranking/retrieval, or computational advertising, with a track record of shipping systems that operate at scale.
  • Expert-level Python and deep proficiency with modern ML frameworks (PyTorch or TensorFlow) and recommendation-specific tooling (e.g., NVTabular, Merlin, Triton).
  • Strong experience with cloud-based ML infrastructure (AWS, Kubernetes, Databricks), containerization (Docker), and model serving at low latency.
  • Advanced SQL skills and experience architecting large-scale data pipelines and feature stores.
  • Demonstrated ability to define technical roadmaps, influence direction across teams, and make architectural decisions that hold up over time.
  • Excellent communication skills with the ability to present complex technical work to executive and non-technical audiences.
  • Be cutting edge. Use the latest AI tools to develop well-designed and robust software.

Preferred qualifications:
  • Experience building and scaling real-time recommendation services handling millions of requests.
  • Expertise in A/B testing methodology, causal inference, or experimentation platforms.
  • Familiarity with LLM-based approaches to recommendation and content understanding.
  • Experience with MLOps practices: model monitoring, feature stores, CI/CD for ML, and automated retraining pipelines.
  • Prior experience technically leading a team of ML practitioners and setting standards adopted by others.

Additional Information
The salary range for this position is $210,000 - $250,000. All positions are currently eligible for an annual profit award or bonus, subject to company results.
Applications for this role will be accepted through July 31, 2026 or until the role is filled. We encourage you to apply early, as we review applications on a rolling basis. Please include your resume and cover letter for consideration. Before applying for any role at Penguin Random House, we recommend you review our applicant resources page and our FAQs page.
Penguin Random House job postings include a good faith compensation range for each open position. The salary range listed is specific to each particular open position and takes into account various factors including the specifics of the individual role, and candidate's relevant experience and qualifications.
Full-time employees are eligible for our comprehensive benefits program. Our range of benefits include, but are not limited to, Medical/Prescription drug insurance, Dental, Vision, Health Care/Dependent Care Flexible Spending Account, Health Savings Account, Pre-Tax and Roth 401(k), Short and Long-Term Disability Insurance, Life/AD&D Insurance, Commuter Benefits, Student Loan Repayment Program, Educational Assistance & generous paid time off.
Penguin Random House values the array of talents and perspectives that a diverse workforce brings. All qualified applicants will receive consideration for employment without regard to race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, or protected veteran status.
All your information will be kept confidential according to EEO guidelines.
Disclosure requirements pertaining to the collection of your personal data:
Responsible for processing the information provided in your application is the company specified in the job advertisement, with its registered office as indicated. The company processes your data for the purpose of establishing an employment relationship on the basis of Art. 6 (1) b GDPR / Section 26 (1) sentence 1 BDSG.
The retention period for your data is determined by the statutory time limits applicable in the respective country, beginning upon completion of the recruitment process. You can find these here.
You can contact the company's Data Protection Officer at the above-mentioned postal address.
Further information on data protection and your rights can be found here.
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