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Remote Google Machine Learning Engineer Jobs in Gilbert, AZ

Senior Data & AI Engineer

Phoenix, AZ · Remote

$100K - $136K/yr

  • PTO

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 ...

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

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

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

... machine learning, statistical modeling, and data analysis. - Proficiency in programming languages such as Python and SQL. - Experience with time series forecasting techniques (e.g., Prophet, ARIMA ...

Sr Digital Analytics Specialist

Gilbert, AZ · On-site +1

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

... based programming. An Ascend Learning brand, NASM has delivered over 1.4 million fitness ... Remote candidates will be considered. HOW YOU'LL SPEND YOUR TIME * Design the end-to-end strategy ...

Showing results 21-40

Remote Google Machine Learning Engineer information

See Gilbert, AZ salary details

$31.4K

$128.4K

$192.9K

How much do remote google machine learning engineer jobs pay per year?

As of Aug 18, 2026, the average yearly pay for remote google machine learning engineer in Gilbert, AZ is $128,358.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,200.00 and $154,500.00 per year, depending on experience, location, and employer.

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 popular job titles related to Remote Google Machine Learning Engineer jobs in Gilbert, AZ?

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

What job categories do people searching Remote Google Machine Learning Engineer jobs in Gilbert, AZ look for?

The top searched job categories for Remote Google Machine Learning Engineer jobs in Gilbert, AZ are:

What cities near Gilbert, AZ are hiring for Remote Google Machine Learning Engineer jobs?

Cities near Gilbert, AZ with the most Remote Google Machine Learning Engineer job openings:

Senior Data & AI Engineer

Hospice of the Valley

Phoenix, AZ • Remote

$100K - $136K/yr

Full-time

PTO

Re-posted 23 days ago


Hospice Of The Valley (Arizona) rating

7.8

Company rating: 7.8 out of 10

Based on 24 frontline employees who took The Breakroom Quiz

12th of 45 rated hospices


Job description

Join Arizona’s largest, most prominent not-for-profit hospice, serving the valley since 1977.

Hospice of the Valley is a national leader in hospice care and has been serving the Phoenix metropolitan area since 1977.  A mission-driven, not-for-profit organization, Hospice of the Valley employs compassionate, skilled professionals who are committed to excellence, enjoy teamwork, and contribute daily to our mission and culture of caring. Team members experience a friendly, supportive atmosphere, leadership support, autonomy, flexibility, and the privilege of doing meaningful, rewarding work.

Benefits:

  • Supportive work environment with a culture of caring for patients and one another.
  • Competitive wages and excellent benefit program.
  • Generous Paid Time Off.
  • Flexible schedules for work/life balance.

Position Profile

The Senior Data & AI Engineer will need to have deep handson experience in Snowflake, Microsoft Fabric (incl. OneLake), and healthcare data ecosystems. The ideal candidate understands data modeling, data integration, and data transformation across structured and unstructured sources, and can build machine learning pipelines that operate on claims and clinical data. You’ll design secure, scalable data platforms; map and normalize data across payers, providers, CMS datasets, EHR systems, and HIEs; and operationalize AI tools to drive measurable outcomes in cost, quality, and member/patient experience.

Responsibilities

Data Platform Engineering

  • Architect, implement, and optimize data solutions in Snowflake and Microsoft Fabric (incl. OneLake, Lakehouses, Warehouses, and Data Engineering pipelines).
  • Build robust ingestion frameworks for batch and streaming data (e.g., ADLS, EventHub, APIs, SFTP) with lineage and governance.
  • Manage data security, privacy, and compliance (HIPAA/PHI; rolebased access, masking, tokenization, deidentification).

Data Modeling & Integration

  • Design conceptual/logical/physical models (normalized, dimensional/star, data vault where appropriate).
  • Implement data mapping and transformations for structured (claims, eligibility, provider, enrollment) and unstructured (clinical notes, PDFs) data.
  • Harmonize healthcare data using FHIR/HL7/CCDA, X12/EDI 837/835, NCPDP, and CMS standards; reconcile and link records across EHR and HIE sources.

Analytics & Machine Learning

  • Build ML pipelines for risk stratification, cost/utilization forecasting, fraud/waste/abuse detection, quality measure computation (e.g., HEDIS), and care gap identification.
  • Operationalize models with MLOps (experiment tracking, reproducibility, CI/CD, monitoring, drift detection).
  • Leverage LLMs/AI tools for data quality, entity resolution, summarization, and clinical insights—ensuring safety, bias checks, and auditability.

Governance & Observability

  • Implement data cataloging, lineage, and metadata (e.g., Microsoft Purview or equivalent).
  • Establish quality SLAs, validation rules, profiling, and automated anomaly detection.
  • Instrument pipelines for cost, performance, and reliability (e.g., Snowflake resource monitors, Fabric capacities).

Collaboration & Delivery

  • Work with product owners, clinicians, actuaries, and analytics teams to translate requirements into scalable solutions.
  • Produce clear documentation, data dictionaries, and mapping specs; mentor engineers and analysts.
  • Contribute to architectural roadmaps, reference patterns, and best practices across the enterprise.

Maintains and enhances professional skills.

Adheres to high standards of personal and professional conduct.

Minimum Qualifications

  • 8+ years in data engineering/analytics; 5+ years handson with Snowflake (compute, storage, virtual warehouses, tasks, streams, Snowpipe, Time Travel, RBAC, row/column masking, data sharing, Dynamic Tables).
  • 2+ years with Microsoft Fabric (including OneLake, Lakehouses, Warehouses, Dataflows Gen2, Notebooks, Pipelines; capacity management).
  • Strong data modeling expertise (dimensional/star, 3NF, data vault; surrogate keys, SCD types, conformed dimensions).
  • Data integration & transformation proficiency: SQL (advanced), dbt or Fabric Dataflows/Power Query M, ADF/Synapse/Fabric Pipelines, Python for ETL/ELT.
  • Mapping from CMS data (e.g., Medicare datasets, claims/encounters), X12/EDI, FHIR/HL7, provider and eligibility.
  • Experience with structured (tables, CSV, Parquet) and unstructured (clinical notes, PDFs, blobs) data; NLP pipelines (optional but valued).
  • Machine learning: feature engineering, model training/evaluation, and deployment (e.g., scikitlearn, PyTorch/TensorFlow, Fabric ML/Notebook, Azure ML); production monitoring.
  • Security & compliance: HIPAA, PHI handling, auditing, data residency, BAAs; practical access control in Snowflake/Fabric.
  • Strong communication; ability to author mapping specs, lineage docs, and present tradeoffs to technical and nontechnical stakeholders.

Preferred Qualifications

  • Interoperability: FHIR R4, HL7 v2, X12/EDI (837/835), NCPDP; experience with HIEs and EHR integrations (Epic, Cerner, etc.).
  • CMS & payer/provider data: Medicare feeforservice, MA, Medicaid, CCW, APCD, and quality programs; risk adjustment (HCC), HEDIS measures.
  • MLOps & DevOps: MLflow, DVC, GitHub Actions/Azure DevOps, containerization (Docker), orchestration (Airflow, Fabric Pipelines, or ADF).
  • Governance: Microsoft Purview (catalog, lineage, classifications), data quality tools.
  • Visualization: Power BI and Fabric Direct Lake; semantic modeling and rowlevel security.
  • Cloud: Azure (ADLS, Event Hub, Functions, Key Vault, Databricks), optional AWS/GCP exposure.
  • Certifications: Snowflake SnowPro Core/Advanced, Microsoft Certified (Azure Data Engineer Associate, Fabric Analytics Engineer).

Hospice of the Valley is an equal employment opportunity employer. EOE/M/F/D/V


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