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Remote Google Machine Learning Engineer Jobs in Arizona

Dutchbros

Lead Engineer, Data Platforms

Tempe, AZ · On-site +1

$111K - $133K/yr

... machine learning, and AI-driven workflows and will be responsible for designing and implementing ... Location Requirement: This position is eligible for remote work within any state Dutch Bros ...

Motorola

Senior DevOps Engineer (US REMOTE)

Phoenix, AZ · Remote

$140K - $170K/yr

... s Full-Stack Engineer with expertise in IaC (Terraform), Helm, MySQL, Kubernetes, and CI/CD ... Experience with AI/machine learning technologies is strongly preferred. * Familiarity with TCP/IP ...

Hospice of the Valley

Senior Data & AI Engineer

Phoenix, AZ · Remote

$100K - $136K/yr

Position Profile The Senior Data & AI Engineer will need to have deep handson experience in ... Analytics & Machine Learning * Build ML pipelines for risk stratification, cost/utilization ...

YO AI Labs

AI Data Science Expert - Remote

Phoenix, AZ · Remote

$100 - $200/hr

Prompt Engineering * AI Output Evaluation * Quality Assurance * Technical Documentation * Technical ... Science, Machine Learning, Applied AI, Statistics, Quantitative Analytics, or Data Analytics.

YO AI Labs

AI Data Scientist Expert - Remote

Phoenix, AZ · Remote

$100 - $200/hr

Prompt Engineering * AI Output Evaluation * Quality Assurance * Technical Documentation * Technical ... Science, Machine Learning, Applied AI, Statistics, Quantitative Analytics, or Data Analytics.

TPI Composites

Engineer, Development

Scottsdale, AZ · On-site +1

Develops numerical models to simulate the manufacturing processes and create engineering tools ... Experience with developing machine learning and artificial intelligence techniques is highly ...

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Remote Google Machine Learning Engineer information

What is a remote Google machine learning engineer?

A Remote Google Machine Learning Engineer is a professional who designs, builds, and deploys machine learning models and artificial intelligence solutions, often using Google Cloud technologies, while working from a remote location. These engineers collaborate with cross-functional teams to solve complex business problems, optimize data pipelines, and improve model performance. Their responsibilities typically include data preprocessing, model selection, training, evaluation, and deployment, all while ensuring scalability and security. Working remotely allows them to contribute to projects from anywhere, leveraging cloud-based tools and collaboration platforms.

What are the key skills and qualifications needed to thrive as a remote Google machine learning engineer?

To thrive as a Remote Google Machine Learning Engineer, you need a strong background in computer science, mathematics, and machine learning algorithms, typically supported by a relevant degree and experience in building scalable models. Proficiency with tools such as TensorFlow, Python, Google Cloud Platform (GCP), and familiarity with distributed systems is essential. Excellent problem-solving, communication, and self-management skills are crucial for effective remote collaboration and innovation. These capabilities enable engineers to deliver impactful machine learning solutions while seamlessly integrating with global Google teams.

How do remote Google machine learning engineers typically collaborate with cross-functional teams while working from different locations?

Remote Google Machine Learning Engineers often use a combination of video conferencing, cloud-based collaboration tools, and shared code repositories to work closely with data scientists, product managers, and software engineers. Regular stand-up meetings, sprint planning sessions, and detailed documentation help ensure everyone is aligned and project milestones are met. Despite being remote, engineers are encouraged to proactively communicate progress, share insights, and participate in code reviews to maintain a strong team dynamic and drive successful project outcomes.

What are the most commonly searched types of Google Machine Learning Engineer jobs in Arizona?

The most popular types of Google Machine Learning Engineer jobs in Arizona are:

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For Remote Google Machine Learning Engineer jobs in Arizona, the most frequently searched job titles are:

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Remote Google Machine Learning Engineer jobs near you

Elastic Security Engineer (SIEM) - Active TS Clearance

SERVISS LLC

Sierra Vista, AZ • Remote

Full-time

Medical, Dental, Vision, Life, Retirement

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

Job description

Elastic Security Engineer (SIEM)Location: Sierra Vista, AZ / Remote
Employment Type: Full-TimeAbout SERVISSAt SERVISS, we deliver cutting-edge cybersecurity and IT solutions to government and commercial clients, with a mission to secure systems, data, and critical infrastructure through innovation and expertise. We don’t just deploy tools; we engineer mission platforms that become foundational systems of record for cyber operations, compliance automation, and national cyber readiness. From CISA CDM to DoW mission enclaves, our work shapes how government sees, governs, and defends its digital terrain.As a provider of managed cybersecurity services, SERVISS delivers a highly tailored offering to each customer. Our mission is broad and our teams are agile; we look to your unique skills to approach and solve problems in your own way, whether engineering a system to clear a technical hurdle, protecting customer data, or consulting across a wide range of security topics. You are empowered to engage and lead across multiple groups.Position SummarySERVISS is seeking an Elastic Security Engineer to support a Federal DoD SIEM program. This is a technical, hands-on role in which you will work within a multi-disciplined team to design, build, secure, maintain, optimize, and document multiple Elastic Stack enterprise solutions (Elasticsearch, Logstash, Kibana, Beats, Machine Learning, and SIEM) deployed globally in a Federal DoD environment, with automation support using Ansible.You will perform continuous data-normalization functions and support the delivery of written technical deliverables such as SOPs and process workflows to optimize tool usage and contribute to new capabilities. Your infrastructure, data pipelines, and reporting automation will directly support internal engineering personnel and external customer requirements.The WorkThis is a hands-on engineering role. The selected candidate will:
  • Design, build, secure, maintain, optimize, and document multiple Elastic Stack enterprise solutions (Elasticsearch, Logstash, Kibana, Beats, Machine Learning, and SIEM) deployed globally in a Federal DoD environment
  • Automate deployment and configuration using Ansible playbooks
  • Perform continuous data-normalization functions across diverse data sources
  • Build data pipelines and reporting automation that directly support internal engineering personnel and external customer requirements
  • Author written technical deliverables such as SOPs and process workflows to optimize tool usage and contribute to new capabilities
Key Responsibilities
  • Support a Federal DoD SIEM program as part of a multi-disciplined engineering team
  • Maintain, optimize, and secure enterprise Elastic Stack solutions deployed globally
  • Contribute to new capabilities and the continuous improvement of tool usage
  • Engage and collaborate across engineering teams and customer stakeholders
Required Qualifications
  • Active Top Secret security clearance
  • US citizenship
  • Compliance with DoD 8140 / 8570 IAT Level II certification prior to start date
  • At least 4 years of hands-on experience in deployment, configuration, and solution development using the Elastic Stack for security and logging use cases (Elastic SIEM experience a plus)
  • Demonstrated experience with the full Elastic Stack: Elasticsearch, Logstash, Kibana, Beats, Machine Learning, and REST API integration
  • Demonstrated ability to use Ansible playbooks

Preferred Qualifications
  • Experience integrating Elasticsearch with external systems (e.g., SOAR tools, threat intelligence platforms)
  • Experience with data management: hot/warm/cold architectures, shard allocation and re-allocation, snapshots and restoration
  • Strong experience evaluating existing Elastic clusters, configuration parameters, indexing, search and query performance tuning, security, and cluster administration
  • Experience integrating Elasticsearch with authentication mechanisms such as SAML, LDAP, and PKI
  • Experience supporting the Elastic Stack in on-prem and SaaS environments, including system monitoring and tuning
  • Experience securing the Elastic Stack and hardening hosting environments
  • Development experience in multiple languages (Python, Bash, PowerShell, Painless, etc.)
  • Experience designing and implementing highly scalable Elastic Stack solutions
  • Experience developing data structures and data mappings from various sources to achieve data normalization using Elastic Common Schema (ECS)
  • Experience developing Logstash and/or ingest pipelines
  • Experience developing custom Kibana visualizations and dashboards
  • Experience developing custom reporting solutions using APIs that leverage Elasticsearch and ElastiCache
  • Experience with end-to-end low-level design, development, administration, and delivery of Elasticsearch-based reporting solutions
  • Strong technical foundation in building reliable, scalable, and supportable systems
  • Experience with Red Hat Enterprise Linux deployment and administration
Freedom to Thrive.
Why Join SERVISS? Our goal as an employer is simple yet profound: to create an environment where you can be your best self, pursue your passions, and enjoy the freedom to thrive both personally and professionally. Your success is our success, and we\'re committed to supporting you every step of the way.
  • Highly competitive compensation and best in class benefits
  • 100% of medical, vision, dental, and life insurance premiums paid for by SERVISS
  • Be part of an exciting company with ground floor opportunities in the Equity Participation Program
  • Opportunities for annual performance bonuses and growth incentives
  • 401(k) retirement plan with 6% dollar for dollar match
 Additional Considerations for HUBZone applicants:
Preference may be given to applicants residing in a federal HUBZone in support of SERVISS LLC’s HUBZone workforce objectives; all qualified candidates are encouraged to apply.
 How to Determine HUBZone Residency:
Candidates can verify HUBZone eligibility by entering their home address into the SBA HUBZone Map at https://maps.certify.sba.gov/hubzone/map. If your residence falls within a designated HUBZone area on the map, you are considered a HUBZone resident for hiring preference purposes.

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