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Remote Rag Jobs in Dublin, CA (NOW HIRING)

Noblesoft Technologies

AI Developer

San Jose, CA · Remote

Remote JD: Core Responsibilities * Design, develop, and deploy autonomous AI agents using ... Integrate memory systems and RAG (Retrieval-Augmented Generation) using vector databases for ...

Workato

Forward Deployed Engineer

Palo Alto, CA · On-site +1

Quartz ranked us the #1 best company for remote workers Responsibilities We are looking for an ... Implement and deploy Retrieval-Augmented Generation (RAG) systems using Workato's Agentic AI ...

DocuSign

Machine Learning Engineer

San Francisco, CA · On-site +1

$164K - $266K/yr

Architect sophisticated Retrieval-Augmented Generation (RAG) pipelines and advanced context ... Employee divides their time between in-office and remote work. Access to an office location is ...

DocuSign

Senior Software Engineer

San Francisco, CA · On-site +1

$144K - $190K/yr

... RAG, MLOps, Python * Drive root cause analysis and implement long-term solutions for production ... Employee divides their time between in-office and remote work. Access to an office location is ...

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Remote Rag information

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How much do remote rag jobs pay per hour?

As of Jul 13, 2026, the average hourly pay for remote rag in Dublin, CA is $24.22, according to ZipRecruiter salary data. Most workers in this role earn between $20.29 and $25.72 per hour, depending on experience, location, and employer.

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

I'm sorry, but 'Remote Rag' does not appear to be a recognized professional occupation. Please provide a valid job title.

What is a Remote RAG (Retrieval-Augmented Generation) specialist?

A Remote RAG specialist is a professional who works with Retrieval-Augmented Generation (RAG) systems, typically in the field of artificial intelligence and machine learning. RAG combines traditional information retrieval techniques with generative models like large language models to provide more accurate and contextually relevant answers to user queries. Remote RAG specialists often build, fine-tune, and maintain these systems while working from a remote location. They may also work on integrating RAG models into applications, improving retrieval accuracy, and customizing outputs based on user needs.

What are some common challenges faced by professionals working in a remote RAG (Responsible AI Governance) role?

Professionals in remote RAG roles often encounter challenges related to cross-functional collaboration and maintaining clear communication, especially when working across different time zones. Ensuring alignment on ethical AI standards and compliance requirements can be complex, as it typically involves coordinating with data scientists, legal teams, and business stakeholders. Staying current with evolving regulatory frameworks and best practices in AI governance is also essential, demanding continuous learning and adaptability. Building trust and rapport within a remote team can require extra effort, but leveraging digital collaboration tools and regular check-ins can help mitigate these challenges.
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Remote Rag jobs near you

AI Engineer: Computer Vision, LLMs & ML (Remote)

Intellus Build

San Francisco, CA • Remote

Full-time

Re-posted 28 days ago

Job description

Intellus Build Make Construction Intelligent

Work with ex-Narvar Founding CTO (Unicorn, $1B+). 6 AI patents. Enterprise AI pedigree: Google DCDE, Oracle, Macy's, Walmart Labs.

We're hiring founding team members to transform how the $12T global construction industry buildsfrom residential to mission-critical infrastructure.

AI Engineer: Computer Vision, LLMs & ML

Engineer the AI driving next-generation construction.

Location: Remote-US (San Francisco Bay Area Hybrid Preferred)

Type: Full-time

Compensation: Founding-team equity (12%) + base salary

Construction creates the world around usyet still runs on WhatsApp, spreadsheets, and gut feel. Sites generate terabytes of data dailyyet none of it tells anyone what to do next.

This isn't a data problem. It's an Intelligence problem.

We're building the nervous system for construction. Think Palantir meets Procore, but actually usable by contractors. Our first customers are already begging for access.

You'll be the founding AI engineer, building alongside our founder and construction industry veterans. Async-first. Remote-friendly. Zero bureaucracy. No meetings about meetings. Just ship code that moves dirt and dollars.

You'll turn cutting-edge LLM and vision research into tools that run on dusty job sites and mobile devices.

About the Role

Intellus Build is the Infrastructure of Truththe AI-native operating system that connects dirt to dollars.

The Problem: Construction sites generate terabytes of unstructured data dailyphotos, documents, videos, sensor readings. Currently, this valuable information goes to waste.

Your Mission: Build AI systems that transform construction chaos into actionable intelligence.

What You'll Build

As the founding AI engineer, you'll tackle problems that don't have Stack Overflow answers:

  • Build RAG systems that understand construction terminologyteach AI the difference between 'pour concrete' and 'poor concrete'
  • Deploy computer vision that detects safety violations from grainy phone photos taken at 6 AM
  • Create AI assistants that answer 'What's the status of the Stanford dorm project?' by reasoning across blueprints, contracts, RFIs, and daily photo logs
  • Design real-time progress tracking that works even when construction sites have terrible WiFi
  • Build domain-aware AI that makes construction sites safer and more efficient
  • Build verification systems that track equipment from PO to energization across complex supply chains
Requirements

You are:

  • Recent graduate from top AI program (Stanford AI Lab, MIT CSAIL, or equivalent) OR 23+ years building production ML systems
  • Focused on practical AI applications, not just research demos
  • Comfortable with the full ML stack: data processing model selection deployment monitoring
  • Able to move quicklyyou prototype in hours, not weeks
Must Have
  • Shipped at least one LLM-based application used by real users
  • Experience with RAG, embeddings, and vector databases
  • Strong Python skills plus PyTorch, TensorFlow, or JAX
  • Ability to explain complex ML concepts to non-technical stakeholders
Nice to Have
  • Computer vision experience (YOLO, Segment Anything, etc.)
  • Published ML research or Kaggle competition medals
  • Experience with construction, manufacturing, or industrial datasets
  • Track record of optimizing inference costs
What You'll Work With

We're flexible on the stack, but likely:

  • LLM APIs: OpenAI, Anthropic, Geminimulti-model approach
  • Orchestration: LangChain, LlamaIndex, or custom frameworks
  • Vector stores: Pinecone, Weaviate, or pgvector
  • ML frameworks: PyTorch, TensorFlow, or JAX

You'll help shape these choices as we build.

Why This Role Matters
  • Real-world impact: Your models will help prevent workplace injuries and save lives
  • Unique datasets: Access to proprietary construction data that competitors don't have
  • Greenfield opportunity: Define the AI strategy from day one
  • Domain expertise: Work directly with construction industry veterans
  • Mission-critical scale: Your models will power verification for facilities where downtime isn't an option
  • Pedigree: A Stanford StartX company (elite sub-1% accelerator)
Ready to Build?

You'll complete two quick assessments to show us what you can do.

Top scorers get interviewed. We move fast. No bureaucracy.

Intellus Build is an equal opportunity employer. We welcome candidates from all backgrounds.