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Google Cloud Machine Learning Engineer Jobs in Alexandria, LA

Controls Engineer SME

Boyce, LA ยท On-site

$77K - $99K/yr

Designed from the ground up to support AI and machine learning workloads, our infrastructure is the ... Applied Digital is seeking a highly skilled Data Center Controls Engineer - Operational Subject ...

These IT issues include hardware, software, applications, network diagnostics, cloud and other ... Provide support of network attached devices such as computers, printers, fax machines, biometric ...

Google Cloud Machine Learning Engineer information

See Alexandria, LA salary details

$21

$57

$80

How much do google cloud machine learning engineer jobs pay per hour?

As of Sep 1, 2026, the average hourly pay for google cloud machine learning engineer in Alexandria, LA is $57.93, according to ZipRecruiter salary data. Most workers in this role earn between $49.38 and $65.96 per hour, depending on experience, location, and employer.

What is a Google Cloud Machine Learning engineer?

Google Cloud Machine Learning Engineers are professionals who design, build, and deploy machine learning models using Google Cloud Platform (GCP) services and tools. They work with large datasets, develop scalable ML solutions, and collaborate with data scientists and software engineers. Their role often includes automating data pipelines, optimizing model performance, and ensuring the reliability and security of ML deployments on the cloud. These engineers have expertise in both machine learning algorithms and cloud infrastructure, making them key contributors to data-driven projects.

What are the key skills and qualifications needed to thrive as a Google Cloud Machine Learning engineer?

To thrive as a Google Cloud Machine Learning Engineer, you need strong programming skills in Python or Java, a deep understanding of machine learning algorithms, and a degree in computer science or a related field. Familiarity with Google Cloud Platform (GCP) services such as Vertex AI, BigQuery, TensorFlow, and relevant certifications like the Professional Machine Learning Engineer certification is highly valuable. Excellent problem-solving abilities, collaboration, and clear communication make someone stand out in this position. These skills and qualities are critical for designing, deploying, and optimizing scalable ML solutions that meet business objectives in cloud environments.

What are some typical cross-functional collaborations for a Google Cloud Machine Learning engineer?

As a Google Cloud Machine Learning Engineer, you'll frequently work alongside data scientists, software engineers, and product managers to design, deploy, and maintain machine learning solutions at scale. Collaboration often involves translating business requirements into machine learning pipelines, integrating models into cloud-based applications, and ensuring that solutions are robust, secure, and scalable. Regular communication with DevOps and infrastructure teams is also common to optimize model deployment and monitor performance. This cross-disciplinary teamwork is crucial for delivering impactful, production-ready AI solutions.

What is the difference between Google Cloud Machine Learning Engineer vs Data Scientist?

AspectGoogle Cloud Machine Learning EngineerData Scientist
Required CredentialsGoogle Cloud certifications, programming skills, ML knowledgeStatistics, data analysis, programming, often with advanced degrees
Work EnvironmentCloud platforms, coding, deploying ML modelsData analysis, modeling, reporting, often in research or business settings
Employer & Industry UsageTech companies, cloud service providers, enterprises using Google CloudVarious industries including finance, healthcare, marketing, research

Google Cloud Machine Learning Engineers focus on developing and deploying ML models on Google Cloud, requiring cloud certifications and coding skills. Data Scientists analyze data, build models, and generate insights, often with advanced degrees. While both roles work with data and ML, the Engineer role emphasizes cloud deployment and infrastructure, whereas Data Scientists focus on data analysis and modeling.

What are popular job titles related to Google Cloud Machine Learning Engineer jobs in Alexandria, LA?

For Google Cloud Machine Learning Engineer jobs in Alexandria, LA, the most frequently searched job titles are:

Infographic showing various Google Cloud Machine Learning Engineer job openings in Alexandria, LA as of June 2026, with employment types broken down into 95% Full Time, 4% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $120,487 per year, or $57.9 per hour.

Controls Engineer SME

Applied Digital Corporation

Boyce, LA โ€ข On-site

$77K - $99K/yr

Full-time

Posted 11 days ago


Job description


About Applied Digital:

At Applied Digital, we are the epicenter of AI innovation, crafting cutting-edge data center solutions tailored for the demands of high-performance computing. Designed from the ground up to support AI and machine learning workloads, our infrastructure is the backbone of tomorrow’s technological advancements, including AI-driven video and generative platforms.


We are:

  • Forward-Thinkers: With a keen eye on current market trends and future innovations, we adapt swiftly and lead technological evolution.
  • Resilient: We navigate complex challenges and emerge stronger, delivering robust and reliable solutions for industry pioneers.
  • Innovative Designers: Leveraging the latest technologies, we create visionary solutions that redefine industry standards.


At Applied Digital, we are committed to solving intricate problems, advancing business initiatives, maximizing operational efficiency, and reducing our carbon footprint. We are a team of resilient, forward-thinking innovators driving the AI revolution.


Position Summary:

Applied Digital is seeking a highly skilled Data Center Controls Engineer – Operational Subject Matter Expert (SME). This role focuses on the design, implementation, maintenance, and optimization of control systems within data centers, ensuring reliable, efficient, and secure operations. The ideal candidate will have deep expertise in building management systems (BMS), electrical power monitoring systems (EPMS), automation, and related infrastructure. As an SME, you will also provide technical leadership, troubleshooting guidance, and strategic input on data center projects.


Key Responsibilities:

  • Design, deploy, and maintain control systems for data center facilities, including BMS, EPMS, and automation technologies to monitor and manage mechanical, electrical, and environmental systems.
  • Troubleshoot complex issues in electrical, mechanical, and control systems, performing root cause analysis and implementing corrective actions to minimize downtime.
  • Collaborate with cross-functional teams, including operations, engineering, and vendors, to innovate and optimize automation processes, ensuring compliance with industry standards and safety protocols.
  • Conduct daily inspections, performance monitoring, and predictive maintenance on critical infrastructure, such as cooling systems, power distribution, and fire suppression.
  • Provide subject matter expertise in areas like electrical and mechanical systems, mentoring junior staff, leading training sessions, and representing the organization in client or stakeholder meetings.
  • Manage projects from inception to completion, including system upgrades, integrations, and evaluations of new technologies for enhanced reliability and efficiency.
  • Ensure adherence to regulatory requirements, perform risk assessments, and contribute to disaster recovery planning for data center operations.
  • Analyze system performance data, generate reports, and recommend improvements to support scalable growth in data center capacity.


Basic Qualifications:

  • Bachelor's degree in Electrical Engineering, Mechanical Engineering, Computer Science, or a related field; advanced certifications (e.g., CDCP, PMP) are a plus.
  • 5+ years of experience in data center operations, controls engineering, or a similar role, with proven expertise in BMS/EPMS and automation systems.
  • Strong knowledge of networking, wired/wireless systems, and infrastructure components like servers, cooling, and power distribution.
  • Proficiency in troubleshooting tools, programming/scripting for automation (e.g., Python, PLC programming), and familiarity with industry standards (e.g., ASHRAE, Uptime Institute).
  • Excellent communication skills for collaborating with teams, vendors, and stakeholders, including the ability to explain complex technical concepts clearly.
  • Experience with hyperscale data centers or mission-critical environments is preferred.