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Remote Full Stack Machine Learning Engineer Jobs in New York

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 ...

Senior Full Stack Engineer

New York, NY ยท Remote

$166K - $215K/yr

Collaborate closely with machine learning/vision engineers, LLM domain experts, and our ... remote and hybrid) ๐Ÿฅจ Free lunch provided in the office in NYC & Austin - you'll never go hungry ...

Senior Full Stack Engineer

New York, NY ยท On-site +1

$166K - $215K/yr

Collaborate closely with machine learning/vision engineers, LLM domain experts, and our ... Learning & Growth stipend Flexible long-term work options (remote and hybrid) Free lunch provided ...

Sr. Lead Machine Learning Engineer (IC)

New York, NY ยท On-site +1

$112K - $147K/yr

Sr. Lead Machine Learning Engineer (IC) As a Capital One Machine Learning Engineer (MLE), you'll be ... Eligibility varies based on full or part-time status, exempt or non-exempt status, and management ...

Senior Machine Learning Engineer

New York, NY ยท Remote

$190K - $250K/yr

We are seeking a Senior Machine Learning Engineer to join our AI & ML team in New York City. You will play a key role in maturing and scaling our machine learning infrastructure, ensuring the ...

Clerkie is a remote-first company, with over 40 employees spanning 4 time zones across the United ... About the role We're looking for a Full-Stack engineer who wants to build, own, and ship end-to-end ...

Showing results 21-40

Remote Full Stack Machine Learning Engineer information

What is a remote full stack machine learning engineer?

A Remote Full Stack Machine Learning Engineer is a professional who designs, develops, and deploys machine learning solutions while working remotely. They handle both the front-end and back-end aspects of machine learning projects, including data preprocessing, model building, API development, and integration with user interfaces or cloud platforms. This role requires expertise in programming, machine learning frameworks, cloud services, and web technologies, allowing them to build end-to-end AI-driven applications from anywhere in the world.

What are the key skills and qualifications needed to thrive as a remote full stack machine learning engineer?

To thrive as a Remote Full Stack Machine Learning Engineer, you need proficiency in programming languages (such as Python or JavaScript), a solid understanding of machine learning algorithms, experience with web development frameworks, and typically a degree in computer science or a related field. Familiarity with tools like TensorFlow, PyTorch, Docker, cloud computing platforms (AWS, GCP), and version control systems (Git) is essential. Strong problem-solving skills, self-motivation, and clear communication are crucial soft skills, especially in remote and cross-functional team environments. These combined skills ensure effective design, deployment, and integration of machine learning solutions in scalable web applications while maintaining productivity in a remote setting.

What are some common challenges faced by remote full stack machine learning engineers, and how can they be addressed?

Remote Full Stack Machine Learning Engineers often encounter challenges such as managing effective collaboration with cross-functional teams and ensuring smooth deployment of machine learning models into production environments. To address these, it's important to establish clear communication channels, regularly participate in virtual stand-ups, and use collaborative platforms such as GitHub and Slack. Additionally, staying organized with version control and thorough documentation helps maintain project transparency and ensures seamless handoffs between backend and frontend development. Proactively seeking feedback and scheduling regular check-ins with team members can further enhance productivity and integration within the team.

What is the difference between Remote Full Stack Machine Learning Engineer vs Remote Data Scientist?

AspectRemote Full Stack Machine Learning EngineerRemote Data Scientist
Primary FocusDeveloping end-to-end machine learning applications, including backend, frontend, and model deploymentAnalyzing data, creating models, and generating insights without necessarily building full applications
Skills RequiredProgramming (Python, JavaScript), ML frameworks, web development, deployment toolsStatistics, data analysis, visualization, Python/R, SQL
Work EnvironmentCollaborates with developers, data engineers, and product teams in tech-driven companiesWorks with data teams, analysts, and business units in various industries

While both roles involve working with data and machine learning, a Remote Full Stack Machine Learning Engineer builds complete applications with integrated ML models, whereas a Remote Data Scientist focuses on data analysis and model creation without necessarily developing full applications.

What are the most commonly searched types of Full Stack Machine Learning Engineer jobs in New York?

The most popular types of Full Stack Machine Learning Engineer jobs in New York are:

What are popular job titles related to Remote Full Stack Machine Learning Engineer jobs in New York?

For Remote Full Stack Machine Learning Engineer jobs in New York, the most frequently searched job titles are:

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

The top searched job categories for Remote Full Stack Machine Learning Engineer jobs in New York are:

What cities in New York are hiring for Remote Full Stack Machine Learning Engineer jobs?

Cities in New York with the most Remote Full Stack Machine Learning Engineer job openings:

Sr. Lead Machine Learning Engineer/Remote

Apetan Consulting llc

Paterson, NJ โ€ข Remote

$80 - $150/hr

Contractor

Re-posted 6 days ago


Job description

Sr. Lead Machine Learning EngineerLocation-RemoteJob Summary

The Sr. Lead Machine Learning Engineer is responsible for leading the design, development, deployment, and optimization of machine learning solutions that drive business value. This role combines technical expertise, strategic leadership, and cross-functional collaboration to build scalable AI/ML systems, mentor engineering teams, and guide the organization's machine learning initiatives.

Key Responsibilities
  • Lead the development and deployment of machine learning models and AI-driven solutions.
  • Design scalable ML architectures, pipelines, and production-ready systems.
  • Collaborate with data scientists, software engineers, product managers, and business stakeholders to define and deliver ML solutions.
  • Oversee data preparation, feature engineering, model training, evaluation, and monitoring processes.
  • Optimize model performance, scalability, reliability, and operational efficiency.
  • Establish best practices for MLOps, model governance, testing, and deployment.
  • Conduct code reviews and provide technical leadership and mentorship to engineering teams.
  • Evaluate emerging AI/ML technologies and recommend innovative solutions.
  • Ensure compliance with security, privacy, and responsible AI standards.
  • Support production systems by troubleshooting and resolving complex ML-related issues.
  • Drive technical roadmaps and contribute to strategic AI initiatives.
Required Qualifications
  • Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related field.
  • 8+ years of software engineering experience, including 5+ years in machine learning engineering.
  • Strong proficiency in Python and machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
  • Experience building and deploying machine learning models in production environments.
  • Strong knowledge of data structures, algorithms, statistics, and machine learning techniques.
  • Experience with cloud platforms such as AWS, Azure, or Google Cloud.
  • Knowledge of MLOps tools, CI/CD pipelines, and model monitoring practices.
  • Excellent leadership, communication, and problem-solving skills.
Preferred Qualifications
  • Master’s degree or Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
  • Experience with large-scale distributed systems and big data technologies.
  • Knowledge of Generative AI, Large Language Models (LLMs), NLP, computer vision, or recommendation systems.
  • Experience with Kubernetes, Docker, and cloud-native architectures.
  • Prior experience leading technical teams and enterprise AI initiatives.