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Remote Machine Learning Robotics Jobs in Georgia

Domain Expert - (STEM PhD)

Atlanta, GA ยท Remote

$80 - $90/hr

Remote micro1 is engaging PhD-level Engineers in Electrical, Mechanical, or Chemical disciplines to ... Experience with or interest in AI, machine learning, or technology-driven projects (a plus, not ...

Remote micro1 is engaging PhD-level Engineers in Electrical, Mechanical, or Chemical disciplines to ... Experience with or interest in AI, machine learning, or technology-driven projects (a plus, not ...

... platforms, machine learning workloads, cloud infrastructure, and data integrations. * Lead root ... We embrace a remote-first culture through our Flexible Workplace. Most employees hold Home-Flex ...

Data Engineer - GCP

Atlanta, GA ยท On-site +1

$110K - $132K/yr

... and machine learning models. What We Are Looking For: We are seeking an experienced and highly ... Flexible work environment and remote work options. Join us and be part of a team building ...

Data Engineer - GCP

Atlanta, GA ยท On-site +1

$110K - $132K/yr

... and machine learning models. What We Are Looking For: We are seeking an experienced and highly ... Flexible work environment and remote work options. Join us and be part of a team building ...

Senior DevOps Engineer (US REMOTE)

Atlanta, GA ยท Remote

$140K - $170K/yr

Experience with AI/machine learning technologies is strongly preferred. * Familiarity with TCP/IP ... Candidate can live anywhere in the United States. #LI-MP2 #LI-REMOTE Basic Requirements * 8+ years ...

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Remote Machine Learning Robotics information

What is a Remote Machine Learning Robotics job?

A Remote Machine Learning Robotics job involves developing and implementing machine learning algorithms to control and improve robotic systems, all while working from a remote location. Professionals in this field use artificial intelligence techniques to enable robots to learn from data and adapt to new tasks. They collaborate with teams virtually, leveraging cloud-based tools and simulation environments to design, test, and deploy robotic solutions. This role typically requires strong programming skills, knowledge of robotics frameworks, and experience with machine learning models.

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

AspectRemote Machine Learning RoboticsRemote Data Scientist
Required CredentialsDegree in Robotics, Computer Science, or related fields; experience with ML algorithms and robotics platformsDegree in Data Science, Statistics, or related fields; proficiency in ML, statistics, and programming
Work EnvironmentHands-on with robotics hardware, simulation environments, and software developmentData analysis, modeling, and visualization primarily on software platforms
Employer & Industry UsageRobotics companies, manufacturing, autonomous vehicles, research labsTech firms, finance, healthcare, research institutions

Remote Machine Learning Robotics focuses on developing intelligent systems that integrate robotics hardware with machine learning algorithms, often requiring hands-on hardware work. In contrast, Remote Data Scientists primarily analyze data and build models using software tools. Both roles involve ML expertise but differ in work environment and industry applications.

How do remote machine learning robotics professionals typically collaborate with hardware teams when working off-site?

Remote machine learning robotics professionals often collaborate closely with hardware teams through regular virtual meetings, shared documentation, and cloud-based development environments. They use simulation tools to test algorithms before deployment and rely on video calls or live streams to observe hardware tests in real time. Effective communication and detailed feedback are essential to ensure that software and hardware integration runs smoothly, despite working from different locations. This collaborative approach helps address issues quickly and keeps projects on track.

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

To thrive as a Remote Machine Learning Robotics Engineer, you need a solid background in robotics, machine learning algorithms, programming (Python, C++), and typically a degree in computer science, robotics, or a related field. Familiarity with robotics frameworks (like ROS), machine learning libraries (such as TensorFlow or PyTorch), and experience with cloud platforms or remote collaboration tools are highly valued. Strong problem-solving abilities, initiative, and effective remote communication skills help you excel in distributed teams. These competencies enable you to develop intelligent robotic systems efficiently, collaborate across locations, and drive innovation in a rapidly evolving field.
What job categories do people searching Remote Machine Learning Robotics jobs in Georgia look for? The top searched job categories for Remote Machine Learning Robotics jobs in Georgia are:
What cities in Georgia are hiring for Remote Machine Learning Robotics jobs? Cities in Georgia with the most Remote Machine Learning Robotics job openings:
Learning and Development Specialist (Remote - Americas)

Learning and Development Specialist (Remote - Americas)

Forbes Travel Guide

Atlanta, GA โ€ข On-site, Remote

Full-time

Re-posted 15 days ago


Job description

At Forbes Travel Guide, we are dedicated to celebrating and elevating excellence in hospitality. As the trusted global authority on luxury service, we empower professionals and organizations to deliver unforgettable guest experiences.
The Learning Technology and Data Specialist is a hands on role that powers our learning ecosystem, connecting the LMS to the broader tech stack, automating data flows, and turning learning data into clear insights. You'll partner closely with our LMS Administrator, and Instructional Designer, to ensure a seamless learner experience and trustworthy reporting for a global audience. This role is instrumental to our upcoming launch timeline and ongoing scale.
What You'll Do (Core Responsibilities)
1. Integrations and Architecture
Own day to day configuration, maintenance, and monitoring of LMS integrations such as HRIS, CRM, SSO, IdP, proctoring, assessment, and content libraries.
Build and maintain API based connections and data syncs, create technical runbooks and incident response guides.
Partner with IT and Security on authentication and authorization, SAML, OIDC, OAuth, SCIM provisioning, and governance.
2. Data, Reporting, and Insights
Design robust data models and pipelines from the LMS to analytics such as Power BI or Tableau, ensuring accuracy and freshness of KPIs.
Develop executive and operational dashboards, including adoption, completion, time to competency, NPS, CSAT, cohort performance, and certification readiness.
Establish data quality rules, reconciliation checks, and documentation for audit and compliance.
3. Automation and Operations
Automate high volume workflows such as user provisioning, enrollments, tagging, certificate issuance, and crediting using RPA, no code tools, or scripts.
Manage release testing and change control, including sandbox validation, UAT coordination, and phased rollouts.
Maintain a transparent backlog and sprint board in Microsoft Planner, and track SLAs for incidents and requests.
4. Vendor and Stakeholder Collaboration
Serve as a technical point of contact with LMS and key vendors such as Honorlock and others, escalating and resolving integration issues.
Translate business requirements from Learning, GTM, and leadership into scalable technical solutions.
Provide enablement for admins and content teams via SOPs, job aids, and quick reference guides.
Near Term Outcomes and Success Metrics
By 30 days
Inventory all current and planned integrations, document data dictionaries and flow diagrams.
Stand up an issue and runbook library, and define SLAs and severity levels.
By 60 days
Deliver version one executive dashboard covering adoption, completion, and compliance KPIs.
Implement automated user provisioning and enrollment rules for at least one priority audience.
By 90 days
Achieve 99.9 percent integration uptime for core systems, and reduce manual admin workload by 25 percent through automation.
Publish data quality scorecards, including accuracy, completeness, and timeliness, and implement remediation playbooks.
Ongoing KPIs
Integration uptime and mean time to recovery.
Data accuracy and reconciliation error rate.
Dashboard adoption, including monthly active viewers and decision use cases.
Hours saved through automation, baseline versus current.
SLA adherence for incidents and requests.
Qualifications
3 to 5 years in learning technologies, data or BI, systems analysis, or adjacent technical roles.
Hands on experience with LMS administration and integrations, Docebo experience a plus, and proctoring or assessment tools such as Honorlock or similar.
Proficiency with APIs, REST, JSON, and basic scripting in PowerShell, Python, or JavaScript, or no or low code automation platforms.
Working knowledge of SSO, IdP, SAML, OIDC, SCIM, and role based access control.
Solid skills in data modeling and BI, Power BI preferred, Excel advanced, plus intermediate SQL for data preparation and validation.
Familiarity with SCORM, xAPI, content packaging, and learning data standards.
Strong documentation habits and cross functional communication skills to translate business to technical.
Preferred Qualifications
Experience with Docebo Learn, Connect, or Content, Honorlock integration patterns, and LMS sandboxes or UAT.
Exposure to middleware or ETL tools such as Boomi, Workato, Azure Data Factory, or Power Automate.
Background in privacy, compliance, and data governance for global audiences such as GDPR, SOC 2, and access logging.
Experience building executive ready dashboards and narratives across product, operations, and revenue perspectives.
Bachelor's degree in IS, Data Analytics, Education Technology, or related field, or equivalent practical experience.
Certifications including Docebo Admin, Microsoft Power BI, Azure or AWS fundamentals, and Security are nice to have.
Tools and Environment (Representative)
LMS: Docebo, sandbox and production.
Proctoring or Assessment: Honorlock and LMS native assessment.
Identity and Access: Azure AD or Entra ID, SAML, OIDC, SCIM.
Data and Analytics: Power BI, Excel, SQL such as Azure SQL or SQL Server or equivalent.
Automation or ETL: Power Automate, Workato, or Boomi as applicable.
Project and Knowledge: Microsoft Planner as source of truth, SharePoint or OneDrive, Confluence or Notion optional.