ZipRecruiter

Log In

2

Remote Embedded Machine Learning Jobs in New Jersey

UVeye

Sales Development Representative

Teaneck, NJ · On-site +1

$65K - $75K/yr

... vision, machine learning, and generative AI within the automotive sector. With over $380M in ... This position can be based remote with eastern time zone preferred or in our Teaneck, NJ office.

QCi

... affordable quantum machines to the world today. QCi products are designed to operate at room ... supporting embedded software or internal tools. * Understanding of resource access, system ...

next page

Showing results 1-20

All JobsRemote Embedded Machine Learning JobsRemote Embedded Machine Learning Jobs in New Jersey

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 Jersey? The most popular types of Embedded Machine Learning jobs in New Jersey are:
What are popular job titles related to Remote Embedded Machine Learning jobs in New Jersey? For Remote Embedded Machine Learning jobs in New Jersey, the most frequently searched job titles are:
What job categories do people searching Remote Embedded Machine Learning jobs in New Jersey look for? The top searched job categories for Remote Embedded Machine Learning jobs in New Jersey are:
What cities in New Jersey are hiring for Remote Embedded Machine Learning jobs? Cities in New Jersey with the most Remote Embedded Machine Learning job openings:
Remote Embedded Machine Learning jobs near you
AI Solutions Architect - Remote

AI Solutions Architect - Remote

NAVA Software Solutions

Jersey City, NJ • On-site, Remote

$69 - $90.75/hr

Full-time

This job post has expired today. Applications are no longer accepted.

Job description

NAVA Software solutions is looking for a AI Solutions Architect
Details:
AI Solution Architect - Insurance Domain (Azure & AWS)
Location: Remote
Duration: 12 months
Role Overview
As an AI Solution Architect specializing in Azure and AWS, you will lead the design, development, and production deployment of large-scale AI/ML solutions tailored for the insurance industry. You will work closely with cross-functional teams including data scientists, engineers, actuaries, and business leaders to transform business strategy into secure, scalable, and cost-effective AI architectures.
Key Responsibilities
AI Strategy & Use Case Development
  • Identify and prioritize AI/ML use cases across the insurance value chain: Underwriting, pricing, claims fraud detection, customer segmentation, policy recommendation engines, and chatbots.
  • Partner with business stakeholders (e.g., actuaries, underwriters, claims analysts) to define impactful AI-driven solutions that enhance decision-making and operational efficiency.
Architecture & Design
  • Design resilient, scalable, and cloud-agnostic AI/ML architectures using Azure and AWS.
  • Build and manage data ingestion and transformation pipelines using Azure Data Factory and AWS Glue.
  • Define and implement MLOps workflows using Azure ML Pipelines, AWS SageMaker Pipelines, and MLflow.
Technical Leadership
  • Lead design reviews, technical workshops, and blueprint sessions.
  • Mentor engineers and data scientists in best practices for model development, deployment, and cloud-native AI.
Solution Development
  • Implement NLP, computer vision, and deep learning solutions using Azure Cognitive Services, AWS Comprehend, Rekognition, and Bedrock.
  • Develop microservices/APIs (Python, FastAPI) for real-time inference and batch scoring.
  • Work with frameworks like TensorFlow, PyTorch, and Scikit-learn.
Integration with Insurance Systems
  • Ensure seamless integration with core insurance platforms: Policy Administration, Claims Management, Billing, CRM (e.g., Guidewire, Duck Creek, Salesforce).
  • Collaborate with enterprise architects to align AI with broader IT modernization initiatives.

Deployment & Operations
  • Containerize models using Docker, deploy via Kubernetes (AKS/EKS).
  • Implement CI/CD automation (Azure DevOps, AWS CodePipeline) and observability (CloudWatch, Prometheus, Azure Monitor).
Governance & Security
  • Enforce cloud security and data compliance using IAM, VNet, KMS, and encryption protocols.
  • Leverage Azure Responsible AI and AWS SageMaker Clarify for explainability, fairness, and auditability.
Stakeholder Engagement
  • Present technical architectures and value propositions to C-level executives, claims directors, and underwriting heads.
  • Serve as the bridge between business needs and AI/ML capabilities.
Required Qualifications
Experience:
  • 8-10 years in AI/ML and software/system architecture.
  • 5+ years in solution/technical leadership roles.

Education:
  • Bachelor's in Computer Science, Data Science, or Engineering.
  • Master's or PhD in AI/ML preferred.

Cloud Expertise:
  • Azure: Azure ML, Cognitive Services, Data Factory, Databricks, Cosmos DB
  • AWS: SageMaker, Comprehend, Rekognition, Glue, Redshift, DynamoDB

Tools & Frameworks:
  • Languages: Python (mandatory), Java or C++
  • ML Frameworks: TensorFlow, PyTorch, Scikit-learn
  • Big Data & Streaming: Spark, Kafka, Hadoop
  • MLOps/DevOps: Kubernetes (AKS/EKS), Docker, MLflow, Kubeflow, CI/CD pipelines
Preferred Qualifications
  • Certifications: Azure AI Engineer Associate, AWS Certified Machine Learning - Specialty
  • Advanced AI Expertise: Generative AI (Azure OpenAI, ChatGPT, AWS Bedrock), Prompt Engineering, Agentic AI
  • Community & Research: Contributions to open-source projects or AI/ML publications
  • Soft Skills: Strong communication, stakeholder management, and strategic thinking. Team leadership and mentoring
NAVA Software Solutions logo

About NAVA Software Solutions

Sourced by ZipRecruiter

NAVA is a strategic partner for companies seeking to develop or customize software and products. Our team of experts leverages cutting-edge technology and deep industry knowledge to provide customized solutions that drive business success. Whether you're looking to improve your operations, increase efficiency, or bring a new product to market, NAVA has the expertise and resources to help you achieve your goals. Trust us to be your partner in software and product development.

Industry

It services

Company size

51 - 200 Employees

Headquarters location

Rocky Hill, CT, US

Social media

Facebook

View All NAVA Software Solutions Jobs

Apply