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Remote Machine Learning Jobs in Mount Pleasant, SC

DataAnnotation

SDLC Engineer - AI Trainer

Charleston, SC · Remote

$50 - $100/hr

Contribute to developing cutting-edge AI systems, while enjoying the flexibility of remote work and ... full-stack, machine learning, and other engineers -- who are driving real-world impact in AI ...

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All JobsRemote Machine Learning JobsRemote Machine Learning Jobs in Mount Pleasant, SC

Remote Machine Learning information

See Mount Pleasant, SC salary details

$24.4K

$40.7K

$84.1K

How much do remote machine learning jobs pay per year?

As of Jun 26, 2026, the average yearly pay for remote machine learning in Mount Pleasant, SC is $40,702.00, according to ZipRecruiter salary data. Most workers in this role earn between $31,100.00 and $44,000.00 per year, depending on experience, location, and employer.

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

To thrive as a Remote Machine Learning Engineer, you need a strong background in mathematics, statistics, programming (often Python), and experience with machine learning frameworks, typically supported by a relevant degree. Familiarity with tools such as TensorFlow, PyTorch, cloud platforms (like AWS or GCP), and version control systems is crucial. Strong problem-solving abilities, self-management, and effective virtual communication distinguish top performers in remote settings. These competencies ensure the engineer can build effective models, collaborate across distributed teams, and deliver impactful solutions independently.

What Are Remote Machine Learning Jobs?

Machine learning is a method of analyzing data via automating analytical model building. The premise is that systems can learn from data. Machine learning positions include machine learning engineer, computer vision engineer, and senior deep learning engineer. In a remote machine learning job, you work from home in a branch of artificial intelligence performing duties related to computational processing and data. Your goal is to design models that solve business problems, such as helping organizations avoid unknown risks or find profitable opportunities. Your responsibilities include maintaining data pipelines, performing model research and implementation, building machine learning systems, and onboarding new utilities.

Can I work remotely as a machine learning engineer?

Yes, many machine learning engineer roles are available for remote work, especially in companies that support flexible or distributed teams. Remote positions often require strong skills in programming, data analysis, and familiarity with tools like Python, TensorFlow, or PyTorch, along with good communication skills. However, some roles may require on-site presence for collaboration or access to specialized hardware.

What is a remote machine learning job?

A remote machine learning job involves working with algorithms, data, and models to develop predictive systems or automate tasks, all while working from a location outside of a traditional office setting. Professionals in this role use techniques from statistics and computer science to analyze data, train machine learning models, and deploy solutions for real-world applications. Remote machine learning jobs can span various industries, including technology, healthcare, finance, and e-commerce. These roles typically require strong programming skills, knowledge of machine learning frameworks, and the ability to communicate findings effectively with team members or stakeholders. Working remotely offers flexibility, but also requires discipline and self-motivation to succeed.

Which 5 jobs will survive AI?

Remote machine learning roles such as data scientists, AI researchers, machine learning engineers, AI product managers, and AI ethics specialists are expected to persist as AI advances. These jobs require specialized skills in programming, statistical analysis, and domain expertise that are difficult to fully automate. Continuous learning and proficiency in tools like Python, TensorFlow, or PyTorch are essential for these roles.

What engineers make $500,000?

Senior machine learning engineers with extensive experience, advanced skills in deep learning and data science, and often working in high-demand industries or at large tech companies can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially in competitive markets.

Are ML jobs in demand?

Machine Learning (ML) jobs are in high demand across various industries such as technology, finance, healthcare, and retail. The growth is driven by increasing adoption of AI solutions, data-driven decision making, and the need for expertise in programming, data analysis, and model deployment, making ML a promising career path.

What are some effective strategies for collaborating with team members while working remotely as a Machine Learning Engineer?

Collaboration in a remote Machine Learning role often relies on clear communication through digital tools such as Slack, Zoom, and project management platforms like Jira or Asana. Regular check-ins and stand-up meetings help keep everyone aligned on project goals and timelines. Sharing code and models via version control systems (like Git) and using collaborative notebooks (such as JupyterHub or Google Colab) are also common practices. Building strong documentation habits and proactively seeking feedback can help ensure smooth teamwork and project success, even across different time zones.

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

AspectRemote Machine LearningData Scientist
Required CredentialsBachelor's/Master's in CS, ML certificationsBachelor's/Master's in CS, Statistics, or related field
Work EnvironmentRemote, collaborative teams, tech companiesRemote or on-site, diverse industries, analytics focus
Industry UsageTech, AI startups, researchFinance, healthcare, e-commerce, tech
Search & Comparison IntentOften compared for technical roles in AI/MLBroader data analysis roles, but overlapping skills

Remote Machine Learning specialists focus on developing algorithms and models primarily in tech environments, often requiring advanced programming and ML knowledge. Data Scientists analyze data to extract insights, sometimes utilizing ML techniques. While both roles share skills and credentials, Remote Machine Learning emphasizes model development, whereas Data Scientists focus on data analysis and interpretation.

More about Remote Machine Learning jobs
What job categories do people searching Remote Machine Learning jobs in Mount Pleasant, SC look for? The top searched job categories for Remote Machine Learning jobs in Mount Pleasant, SC are:
What cities near Mount Pleasant, SC are hiring for Remote Machine Learning jobs? Cities near Mount Pleasant, SC with the most Remote Machine Learning job openings:
Remote Machine Learning jobs near you
Azure Data & AI Architect - Remote US

Azure Data & AI Architect - Remote US

eGroup Enabling Technologies

Charleston, SC • On-site, Remote

$135K/yr

Full-time

Posted 26 days ago

Job description

eGroup is a remote-first technology consulting organization and Microsoft partner, focused on delivering thoughtful, reliable solutions while building lasting relationships-with our clients and with one another. We are a nine-time Microsoft Partner of the Year award winner, reflecting our deep technical expertise and commitment to excellence. Our people are at the center of our success, and we believe great work happens when employees feel trusted, supported, and respected. We are committed to integrity, collaboration, accountability, and creating an inclusive workplace where individuals can do meaningful work and continue to grow.
eGroup is hiring a Azure Data & AI Architect to design and personally deliver modern data and AI solutions in the Microsoft ecosystem (Azure AI services, Microsoft Fabric, and Azure data platforms).
This is a hands-on consulting role - approximately 80% delivery / build work, with the remainder focused on continuous learning and occasional pre-sales support.
You'll work directly with clients to understand their goals, translate ambiguity into a clear technical plan, and deliver solutions that are secure, scalable, and explainable to both technical and non-technical stakeholders.
  • This role is not research-only, prompt-only, or slideware-focused. You will be expected to ship real solutions.
  • This role is client-facing. Clear, timely communication and follow-through are part of the job.
  • We review applications for specificity and real project ownership. If your application is generic, overly polished, or doesn't show what you built, it will not move forward.

Application requirement: Please answer the three short questions within this posting. Applications without these answers will not be reviewed.
Key Responsibilities
Lead End-to-End Solution Delivery
  • Own delivery end-to-end: requirements → architecture → design → build → test → deployment
  • Serve as the technical lead: set engineering standards, unblock delivery, and ensure quality across testing and release

Architect AI Solutions
  • Design and implement AI solutions using Azure AI services (e.g., Azure OpenAI, Cognitive Services, Azure Machine Learning) and, where appropriate, Copilot / Copilot Studio
  • Emphasize security, governance, and real-world usability over experimentation for its own sake

Develop Modern Data Platforms
  • Build data platforms using Azure data services (ADF, Databricks, Synapse, ADLS) and/or Microsoft Fabric
  • Deliver production-grade pipelines (ETL/ELT), storage and compute patterns, and data models supporting BI and AI workloads

Leverage Automation & Power Platform (Bonus Skill)
  • Use Power Platform (Power Apps, Power Automate, Power BI) when it's the right tool for rapid business value
  • Power Platform experience is a plus, not the primary focus

Collaborate Across Teams
  • Partner with engineers and business stakeholders to translate requirements into buildable designs
  • Coordinate with security and governance teams to ensure solutions meet best practices and client constraints

Client Engagement & Communication
  • Communicate architecture decisions - including tradeoffs - in plain language
  • Act as a trusted advisor: align scope to outcomes, manage expectations, and keep clients informed when things change

Innovation & IP Development
  • Create reusable internal assets (reference architectures, accelerators, playbooks) that improve delivery quality

Continuous Learning
  • Stay current on Azure AI and data platform changes and apply learning pragmatically to client solutions

Pre-Sales Support (As Needed)
  • Support early discovery, solution envisioning, and contribution to technical proposals, estimates, and SOWs

Required Qualifications
Experience
  • 10+ years delivering technology solutions, including 5+ years leading data engineering, analytics, or AI work on Azure
  • Clear evidence of personal ownership - you can explain what you built, why you chose it, and what you'd change next time

Azure & Data Expertise
  • Hands-on experience with Azure AI services and Azure data platforms and/or Microsoft Fabric

Architecture Judgment
  • Ability to design scalable patterns (lakes, warehouses, modeling) and integrate AI responsibly into workflows
  • Practical operational mindset (CI/CD and MLOps experience is a plus)

Problem-Solving & Adaptability
  • Comfortable working in ambiguity, making tradeoffs explicit, and keeping delivery moving without hand-holding

Communication & Leadership
  • Strong client-facing communicator who can explain complex decisions clearly and lead technical direction confidently

Education
  • Bachelor's degree in Computer Science, Engineering, or equivalent professional experience

Preferred Qualifications
  • Consulting or professional services background
  • Power Platform and Power BI experience
  • Microsoft Fabric and data governance familiarity
  • Azure DevOps, CI/CD, and infrastructure-as-code experience
  • Relevant Microsoft certifications
  • Python used in real production systems
  • Experience writing and maintaining code for data processing, automation, or model deployment

If you want to do real client delivery at the intersection of data, AI, and Microsoft, and you enjoy owning outcomes from early discovery through go-live, we'd love to meet you.

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