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Remote Embedded Machine Learning Jobs in New York

Alignerr

Machine Learning Engineer

Manhattan, NY · Remote

$154K/yr

Machine Learning Engineer (AI Data Trainer) About the Role What if your machine learning expertise ... Remote * Commitment : 10-40 hours/week What You'll Do * Construct precise, well-structured ...

Inizio Partners

Machine Learning Engineer (GCP)

Manhattan, NY · Remote

$58.25 - $79.75/hr

Location- Remote Overview: As a GCP ML Engineer, you'll design, develop, and maintain machine learning pipelines and infrastructure on the Google Cloud Platform (GCP). You'll work closely with data ...

Finch Services

Machine Learning Engineer

Manhattan, NY · On-site +1

$180K - $280K/yr

As a Machine Learning Engineer at Finch, you'll own the full lifecycle of AI systems, from ... for a remote-first role -- this one is 4 days/week in our NYC office. Compensation The expected ...

Alignerr

Senior Machine Learning Expert

Manhattan, NY · Remote

$95K - $118K/yr

Senior Machine Learning Expert (AI Training) About the Role What if your deep expertise in machine ... This is a fully remote, flexible contract role built for senior-level ML professionals who ...

Autodesk

Senior Machine Learning Engineer

New York, NY · On-site +1

$114K - $157K/yr

This role is currently open to remote work. Candidates must be located near one of our hub ... Design and implement machine learning capabilities that improve Autodesk's customer-facing ...

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Remote Embedded Machine Learning information

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

To thrive as a Remote Embedded Machine Learning Engineer, you need a solid background in embedded systems, machine learning algorithms, and programming languages like C/C++ and Python, often supported by a degree in computer science, electrical engineering, or related fields. Familiarity with microcontrollers, edge AI frameworks (such as TensorFlow Lite or Edge Impulse), and version control systems is typically required. Strong problem-solving skills, effective communication, and self-motivation are essential soft skills for collaborating remotely and troubleshooting complex issues. These skills ensure successful deployment of intelligent solutions on resource-constrained devices and effective teamwork in distributed environments.

What is a Remote Embedded Machine Learning Engineer?

A Remote Embedded Machine Learning Engineer is a professional who develops and deploys machine learning models on embedded systems like microcontrollers, IoT devices, and edge hardware, all while working remotely. Their work involves optimizing algorithms to run efficiently on devices with limited computing power, memory, and battery life. These engineers typically use frameworks such as TensorFlow Lite or TinyML to design intelligent features that operate directly on hardware, enabling real-time decision-making without relying heavily on cloud connectivity. They collaborate with cross-functional teams and often troubleshoot both software and hardware issues from a remote location.

What is the difference between Remote Embedded Machine Learning vs Remote Data Scientist?

AspectRemote Embedded Machine LearningRemote Data Scientist
Required CredentialsBachelor's or Master's in Computer Science, Electrical Engineering, or related fields; experience with embedded systems and ML frameworksBachelor's or Master's in Data Science, Statistics, or related fields; proficiency in data analysis and ML algorithms
Work EnvironmentEmbedded hardware devices, IoT systems, real-time processing environmentsCloud platforms, data analysis labs, remote offices
Employer & Industry UsageTech companies, IoT device manufacturers, automotive, roboticsFinance, healthcare, marketing, tech firms

Remote Embedded Machine Learning specialists focus on integrating ML models into embedded hardware for real-time applications, often working with IoT and robotics. In contrast, Remote Data Scientists analyze large datasets to extract insights, primarily working in cloud or office environments. Both roles require strong analytical skills but differ in technical focus and work settings.

What are some common challenges faced by Remote Embedded Machine Learning Engineers, and how can they be addressed?

Remote Embedded Machine Learning Engineers often encounter challenges related to hardware access, debugging embedded devices remotely, and collaborating with cross-functional teams across time zones. To address these, it's important to set up robust remote development environments, use simulation tools when physical hardware isn't available, and establish clear communication channels for effective teamwork. Regular virtual meetings and detailed documentation also help ensure alignment and smooth progress, despite the remote nature of the work.
What are the most commonly searched types of Embedded Machine Learning jobs in New York? The most popular types of Embedded Machine Learning jobs in New York are:
What are popular job titles related to Remote Embedded Machine Learning jobs in New York? For Remote Embedded Machine Learning jobs in New York, the most frequently searched job titles are:
What job categories do people searching Remote Embedded Machine Learning jobs in New York look for? The top searched job categories for Remote Embedded Machine Learning jobs in New York are:
What cities in New York are hiring for Remote Embedded Machine Learning jobs? Cities in New York with the most Remote Embedded Machine Learning job openings:
Remote Embedded Machine Learning jobs near you

Remote Machine Learning Engineer

Angenex

Jersey City, NJ • Remote

Other

Posted 14 days ago

Job description

Remote Machine Learning Engineer

Jersey City, NJ, United States

About the Job

We're seeking an outstanding ML Engineer to join our data team and help build out best-in-class machine learning solutions on our platform, powering innovative solutions in marketing & sales and commercial analytics.

Responsibilities

- Build and deploy the ML pipelines that power the company machine learning platform.

- Manage MLOps infrastructure to monitor and optimize models.

Qualifications

Experience:

1+ years of professional experience as a Machine Learning Engineer or production-focused Data Scientist.

Proficiency across topics in machine learning and statistics.

Fluency in Python coding as well as data manipulation (SQL, Spark, Pandas)

Broad familiarity with the Python ecosystem and common libraries including Scikit-Learn, XGBoost, PyTorch, Keras, Tensorflow, Pandas, and common ML cloud services.

Familiarity with CNNs, RNN, LSTMs, and the latest research trends.

Experience implementing, deploying, and maintaining production machine learning systems.

Experience monitoring and optimizing model performance.

Experience with Linux, Docker and AWS, and basic development operations.

Advanced degree in computer science, mathematics, statistics or related area of study strongly preferred.

Apply