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Remote Machine Learning Jobs in Albuquerque, NM (NOW HIRING)

Senior AI Systems Engineer

Albuquerque, NM · On-site +1

$95K - $130K/yr

Operationalize machine learning workflows and support AI-enabled applications from development ... This position may be performed fully remote, hybrid, or onsite at an ARA office. Preference will be ...

Senior AI Technologist

Albuquerque, NM · On-site +1

$50.50 - $65/hr

Experience preparing and transforming data for analytics, machine learning, search, or AI-enabled ... This position may be performed fully remote, hybrid, or onsite at an ARA office. Preference will be ...

Data Engineer

Albuquerque, NM · On-site +1

$111K - $133K/yr

Remote Work: No Job Number: R0237241 Location: Albuquerque,NM,US Share job via: Share Data Engineer The Opportunity : Ever-expanding technology like IoT, machine learning, and artificial intelligence ...

Remote Machine Learning information

See Albuquerque, NM salary details

$24.7K

$41.3K

$85.3K

How much do remote machine learning jobs pay per year?

As of Jul 25, 2026, the average yearly pay for remote machine learning in Albuquerque, NM is $41,277.00, according to ZipRecruiter salary data. Most workers in this role earn between $31,500.00 and $44,600.00 per year, depending on experience, location, and employer.

What engineer makes $500,000 a year?

Senior machine learning engineers with extensive experience, advanced skills in deep learning and data modeling, and often working at large tech companies or in specialized industries can earn salaries approaching or exceeding $500,000 annually. Compensation may include base salary, bonuses, and stock options, especially in high-demand markets.

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.

How to make 2000 a week working from home?

Remote machine learning professionals can earn $2,000 or more weekly by taking on high-paying freelance projects, consulting roles, or working for companies that offer remote positions with competitive salaries. Building specialized skills in programming, data analysis, and tools like Python, TensorFlow, or cloud platforms can increase earning potential. Consistent work, a strong portfolio, and networking are key to reaching this income level from home.

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.

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.

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.

Are there remote machine learning jobs?

Yes, remote machine learning jobs are widely available across various industries, often requiring skills in programming, data analysis, and familiarity with tools like Python, TensorFlow, or PyTorch. Many companies offer flexible schedules and remote work options for qualified candidates, especially in tech and research sectors.

Will MLE be replaced by AI?

Machine Learning Engineers (MLEs) design, develop, and deploy AI models, and their role involves understanding algorithms, data preprocessing, and model optimization. While AI automation tools can handle certain tasks, MLEs are essential for creating, fine-tuning, and maintaining complex AI systems, making complete replacement unlikely in the near term.
What are the most commonly searched types of Machine Learning jobs in Albuquerque, NM? The most popular types of Machine Learning jobs in Albuquerque, NM are:
What are popular job titles related to Remote Machine Learning jobs in Albuquerque, NM? For Remote Machine Learning jobs in Albuquerque, NM, the most frequently searched job titles are:
What cities near Albuquerque, NM are hiring for Remote Machine Learning jobs? Cities near Albuquerque, NM with the most Remote Machine Learning job openings:
Infographic showing various Remote Machine Learning job openings in Albuquerque, NM as of July 2026, with employment types broken down into 1% As Needed, 72% Full Time, 24% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $41,277 per year, or $19.8 per hour.

Senior AI Systems Engineer

Berriehill Research

Albuquerque, NM • On-site, Remote

$95K - $130K/yr

Full-time

Posted 22 days ago


Job description

Essential Functions:

  • Lead the deployment, integration, and operational support of AI platforms, tools, and services, ensuring compatibility with existing systems and enterprise processes.
  • Design, implement, monitor, and optimize AI infrastructure, working with server, cloud, and platform engineering teams.
  • Operationalize machine learning workflows and support AI-enabled applications from development through production deployment and sustainment.
  • Build and maintain CI/CD and MLOps pipelines for model packaging, testing, deployment, rollback, and lifecycle management.
  • Implement infrastructure automation using scripting, Infrastructure as Code, and configuration management practices.
  • Provide ongoing technical support, troubleshooting, root cause analysis, and documentation for AI platforms and user-facing AI services.
  • Maintain observability across AI systems through logging, metrics, performance monitoring, alerting, and incident response practices.
  • Ensure security, compliance, and governance requirements are met, including participation in audits, vulnerability management, and secure architecture reviews.
  • Assess and implement system enhancements to improve performance, scalability, reliability, and cost efficiency.
  • Collaborate across divisions to support diverse AI initiatives and align technical implementations with mission and business objectives.
  • Evaluate emerging AI tools, frameworks, and infrastructure approaches for operational fit, supportability, and long-term value.
  • Develop and maintain technical documentation, runbooks, architecture diagrams, and operational procedures.

Experience and Skills Required:

  • Bachelor’s degree in computer science, Engineering, Information Technology, or a related STEM field with 8-10 years of engineering experience. 
  • 2+ years of experience supporting AI/ML platforms, MLOps workflows, model deployment, or AI-enabled infrastructure.
  • Strong coding and automation skills in Python, Bash, or similar scripting languages.
  • Experience with AI/ML frameworks and tooling such as PyTorch, Hugging Face, or similar ecosystems.
  • Proficiency with DevOps and MLOps practices, including CI/CD pipelines, Git-based workflows, containerization, and Kubernetes.
  • Experience deploying AI/ML models or AI services into operational environments, including containerized, cloud, or high-performance computing environments.
  • Familiarity with security frameworks and compliance standards such as NIST and CMMC.
  • Familiarity with AI security functionality in enterprise environments including OAuth
  • Strong communication skills and the ability to collaborate effectively across technical and non-technical teams.

Preferred:

  • Advanced degree or certifications related to AI or machine learning.
  • Experience integrating AI models into scientific workflows.
  • Familiarity with large language model (LLM) APIs and orchestration frameworks such as OpenAI, Hugging Face, LangGraph, or LangChain.
  • Experience with model serving, inference optimization, or AI platform tools such as MLflow, Kubeflow, vLLM, or similar.
  • Experience with simulations for scientific or engineering projects, particularly physical systems simulations.
  • Experience with GPU-based systems or running AI models in HPC environments.
  • Experience writing and deploying MCP Servers on Kubernetes
  • DoD experience
  • Secret Security Clearance – Active or Inactive

Education:

  • Bachelor’s degree in CS, Software Engineering or other IT-related field or equivalent experience

REMOTE WORK NOTICE: This position may be performed fully remote, hybrid, or onsite at an ARA office. Preference will be given to candidates located onsite in the Albuquerque, NM and Raleigh, NC area.