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Remote Ai Data Engineer Jobs in Riverside, CA (NOW HIRING)

Principal Data Architect

Irvine, CA · Remote

$126K - $214K/yr

Principal Data Architect Full-time Remote Exclusive confidential search -- details shared with ... You will partner with product, engineering, analytics, ML, finance, risk, and customer-facing teams ...

Sr. Software Development Engineer - Gen AI

Redlands, CA · On-site +1

$123K - $162K/yr

If you are passionate about geospatial technologies, AI-driven data quality management, and ... Build scalable ML pipelines and data engineering processes to improve overall system architecture

Data Scientist II

Irvine, CA · On-site +1

$82K - $127K/yr

Working closely with product managers, engineering teams, and business stakeholders, this position ... This position is open to remote or hybrid. ESSENTIAL FUNCTIONS & RESPONSIBILITIES: * Mine and ...

Working closely with product managers, engineering teams, and business stakeholders, this position ... This position is open to remote or hybrid. ESSENTIAL FUNCTIONS & RESPONSIBILITIES: * Mine and ...

Working closely with product managers, engineering teams, and business stakeholders, this position ... This position is open to remote or hybrid. ESSENTIAL FUNCTIONS & RESPONSIBILITIES: * Mine and ...

SDET, Remote opportunity

Irvine, CA · On-site +1

$130K - $145K/yr

AI-first team where tooling and process are built around accelerating delivery * Competitive salary ... Verify compliance-critical behavior: disclosure timing, fee tolerances, audit trails, and data ...

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Showing results 1-20

Remote Ai Data Engineer information

See Riverside, CA salary details

$46.4K

$135.3K

$185.2K

How much do remote ai data engineer jobs pay per year?

As of Jul 29, 2026, the average yearly pay for remote ai data engineer in Riverside, CA is $135,329.00, according to ZipRecruiter salary data. Most workers in this role earn between $119,500.00 and $143,400.00 per year, depending on experience, location, and employer.

What are some common challenges faced by Remote AI Data Engineers, and how can they be addressed?

Remote AI Data Engineers often encounter challenges such as coordinating with cross-functional teams across different time zones, ensuring data security when accessing sensitive datasets remotely, and maintaining effective communication for project updates. To address these, it's important to establish clear protocols for data sharing, leverage collaboration tools (like Slack or Jira), and schedule regular check-ins to align with team goals. Adopting strong version control practices and automated testing can also help streamline workflows and minimize errors in a distributed environment.

What is the difference between Remote Ai Data Engineer vs Data Scientist?

AspectRemote Ai Data EngineerData Scientist
Required CredentialsBachelor's in CS, Data Engineering, or related; experience with cloud platformsBachelor's or higher in CS, Statistics, or related; strong analytical skills
Work EnvironmentData pipelines, cloud infrastructure, codingData analysis, statistical modeling, visualization
Employer & Industry UsageTech companies, AI firms, startupsResearch institutions, tech companies, finance
Common Search & ComparisonYesYes

Remote Ai Data Engineers focus on building and maintaining data pipelines and infrastructure for AI applications, requiring skills in data engineering and cloud platforms. Data Scientists analyze data, develop models, and generate insights. While both roles work with data, Data Engineers prepare the data environment, whereas Data Scientists interpret and model the data. They often collaborate but serve different functions in AI and data projects.

What is a Remote AI Data Engineer?

A Remote AI Data Engineer is a professional who designs, builds, and maintains data pipelines and infrastructure to support artificial intelligence (AI) and machine learning (ML) projects, all while working from a remote location. They are responsible for collecting, cleaning, transforming, and storing large datasets, ensuring data quality and accessibility for AI applications. These engineers collaborate with data scientists, software engineers, and stakeholders to deliver data solutions that power intelligent systems, often leveraging cloud technologies and distributed computing. Their work enables organizations to harness data for predictive analytics, automation, and decision-making—without being tied to a physical office.

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

To thrive as a Remote AI Data Engineer, you need strong programming skills (Python, SQL), a solid understanding of data structures, machine learning principles, and typically a degree in computer science or related fields. Familiarity with big data platforms (such as Hadoop or Spark), cloud services (AWS, GCP, or Azure), and experience with AI/ML frameworks like TensorFlow or PyTorch are commonly required. Excellent problem-solving, communication, and self-motivation skills help you collaborate effectively and manage projects independently in a remote setting. These skills and qualities ensure robust AI data pipelines, effective model deployment, and seamless teamwork across distributed environments.
What job categories do people searching Remote Ai Data Engineer jobs in Riverside, CA look for? The top searched job categories for Remote Ai Data Engineer jobs in Riverside, CA are:
What cities near Riverside, CA are hiring for Remote Ai Data Engineer jobs? Cities near Riverside, CA with the most Remote Ai Data Engineer job openings:

Data Scientist, AI Data Foundations

NextDeavor Inc.

Irvine, CA • Remote

$114K - $175K/yr

Contractor

Posted 26 days ago


Job description

Data Scientist, AI Data Foundations
Full-time
Remote
Exclusive confidential search — details shared with qualified applicants.
 
Become a Key Player as a Data Scientist, AI Data Foundations

You will design and build the curated data structures that AI and ML applications consume, enabling higher-quality model training and inference. You will partner with model builders, product, risk, and growth stakeholders to surface actionable insights and ship production-ready vector, feature, and graph data assets. This is a Remote role.

Here's How You'll Make an Impact on the Team
  • Build and maintain vector stores for RAG, including embedding pipelines, chunking strategies, indexing, and refresh patterns.
  • Own the feature store: design, build, and operate feature definitions, freshness SLAs, lineage, and point-in-time correctness for offline/online use.
  • Design and implement graph data structures to model relationships across applicants, applications, products, lenders, decisions, and outcomes.
  • Lead data discovery: profile lending, deposit, and behavioral datasets to identify trends, segments, anomalies, and model drivers; produce actionable hypotheses for stakeholders.
  • Engineer curated, AI-ready datasets with appropriate quality checks, documentation, and governance for downstream model builders and analysts.
  • Define and run evaluation frameworks for RAG retrieval quality, feature drift, embedding quality, and graph completeness; iterate on metrics.
  • Partner closely with ML engineers and applied scientists to ensure data assets accelerate model development and serving workflows.
  • Champion responsible data use by collaborating with governance, security, and compliance teams to ensure data classification, consent, and regulatory boundaries are respected.
  • Communicate findings via write-ups, notebooks, dashboards, and short presentations for technical and non-technical audiences.
Here's What You'll Need to Be Successful in This Role
  • 4–7 years of experience in data science, ML engineering, or applied data roles, with significant time building data assets consumed by models or applications.
  • Hands-on experience designing and operating vector stores for RAG or semantic search (embedding generation, chunking, indexing, retrieval evaluation).
  • Experience building or operating a feature store (e.g., Databricks Feature Store, Feast, or custom), including offline training and online serving patterns and point-in-time correctness.
  • Experience modeling and building graph data structures and writing graph queries (Neo4j, TigerGraph, Cosmos DB Gremlin, or similar).
  • Strong proficiency in Python (pandas, NumPy, scikit-learn, PySpark) and SQL; comfortable using Databricks notebooks and jobs.
  • Practical experience with embedding models and LLM tooling (Hugging Face, OpenAI/Azure OpenAI APIs, LangChain or similar) in production or near-production contexts.
  • Demonstrated data discovery skills: profiling messy datasets, surfacing patterns, validating findings statistically, and explaining results clearly.
  • Solid grounding in classical ML concepts (supervised vs. unsupervised learning, train/test discipline, leakage, evaluation metrics).
  • Strong written and verbal communication skills for technical and business audiences.
Here's What Else Might Help You Out
  • Experience in SaaS or FinTech, especially with lending, deposit, credit, fraud, or KYC/AML data.
  • Familiarity with Databricks-native AI/ML tooling: Databricks Vector Search, Databricks Feature Store, MLflow, Unity Catalog.
  • Experience with open-source vector DBs (pgvector, Pinecone, Weaviate, Chroma, FAISS) and strong opinions on trade-offs.
  • Experience with Microsoft Azure data and AI services (Azure OpenAI, Azure AI Search, ADLS Gen2).
  • Experience evaluating RAG systems end-to-end (recall@k, faithfulness, answer quality, hallucination measurement).
  • Exposure to graph algorithms (community detection, link prediction, centrality) applied to business problems.
  • Bachelor's or Master's in CS, Statistics, Mathematics, Engineering, or related quantitative field, or equivalent experience.
Pay Range

$114,000 - $175,000/year

Ready to Make Your Mark?

This role may fill quickly. Submit your resume to be considered.

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