---
title: 'Senior AI Engineer – LLM, RAG at BrightAI Corporation'
canonical: 'https://feeny.ai/job/senior-ai-engineer-llm-rag-brightai-corporation-palo-alto-10vr74j6k2b3'
type: 'job'
last_seen: '2026-09-13'
---

# Senior AI Engineer – LLM, RAG at BrightAI Corporation

- **Company:** BrightAI Corporation
- **Location:** Palo Alto, CA
- **Posted:** 2025-08-08
- **Last confirmed live:** 2026-09-13
- **Apply:** https://job-boards.greenhouse.io/brightai/jobs/5616545004

## Job description

Sr. AI Engineer – LLM, RAG

BrightAI is a high-growth Physical AI company transforming how businesses interact with the physical world through intelligent automation. Our AI platform processes visual, spatial, and temporal data from billions of real-world events—captured across edge devices, mobile sensors, and cloud infrastructure—to enable intelligent decision-making at scale.

We are now hiring a Sr. AI Engineer – LLM, RAG to lead the development of Retrieval-Augmented Generation (RAG) systems that harness the power of large language models (LLMs) and real-world knowledge sources. This role is pivotal to building next-generation intelligent assistants that help technicians and operators troubleshoot complex issues in industrial settings.

You’ll work at the intersection of NLP, foundational models, and real-time information systems—developing intelligent tools that turn manuals, technician notes, and sensor data into actionable, conversational guidance for the physical world.

## Responsibilities

- Lead the architecture and development of RAG systems that combine LLMs (e.g., LLAMA, Mistral, Claude, GPT) with structured and unstructured external information sources.
- Develop AI-powered assistants to support technicians in diagnosing and resolving anomalies or failures in factory, plant, or industrial settings.
- Build pipelines to ingest, preprocess, and index large corpora of documents (manuals, logs, notes, procedures) for semantic search and grounding.
- Customize and fine-tune foundational models to incorporate domain-specific language, tone, and logic for industrial troubleshooting scenarios.
- Collaborate with product, data, and cloud teams to design scalable, privacy-compliant, and latency-sensitive LLM applications.
- Design evaluation strategies to measure performance, accuracy, and user experience of RAG-enabled systems in production settings.
- Stay up to date with the latest advances in LLM architectures, retrieval methods, and prompt engineering, and integrate emerging techniques into the product roadmap.

Educational Background

- M.S. or Ph.D. in Computer Science, AI, Machine Learning, or a related field, with specialization in NLP or deep learning.
- Strong research or applied background in large language models (LLMs) and retrieval-augmented generation (RAG) systems. Agentic RAG experience is highly desirable.

Required Skills & Expertise

- 5+ years of experience in machine learning or AI with a strong focus on NLP, LLMs, or conversational AI.
- Fluency with modern LLMs and open-source foundational models (e.g., LLAMA, Falcon, Mistral, GPT, Claude).
- Experience building RAG pipelines with tools like LangChain, LlamaIndex, or custom vector database integrations, with at least one production grade system was built.
- Fluency with prompt engineering, instruction tuning, or fine-tuning open-source models.
- Deep understanding of document retrieval (semantic search, embedding generation, similarity metrics) and vector stores (e.g., FAISS, Weaviate, Pinecone).
- Strong foundation in core machine learning techniques, including experience with reinforcement learning (RL) or decision-making models.
- Proficiency with ML development frameworks such as PyTorch, Hugging Face Transformers, or similar.  Strong Python programming is a must.
- Experience integrating AI systems into real-world applications with user-facing interfaces and operational constraints.
- Excellent problem-solving and critical thinking skills; ability to design solutions for complex, ambiguous problems.
- Strong written and verbal communication skills, with ability to collaborate cross-functionally with engineers, product managers, and domain experts.

Bonus Qualifications

- Experience applying LLMs in industrial or physical infrastructure settings (e.g., manufacturing, logistics, utilities, energy).
- Knowledge of industrial control systems, maintenance workflows, or technician support processes.
- Exposure to multimodal models or integrating textual data with sensor and/or time-series data.
- Prior experience in a startup or a fast-paced environment building LLM-powered products from the ground up.

## About BrightAI Corporation

## Company Overview
- **One-liner**: BrightAI provides a Physical AI platform that makes the invisible state of critical infrastructure visible and actionable, enabling proactive operations.
- **Entity Type**: Private (Series A, $51M raised in July 2025)
- **Headquarters**: Palo Alto, California, United States
- **Founded**: 2019
- **Founders**: Alex Hawkinson, Nathan Hanks

## Core Business
- **Primary Industry**: AI-powered critical infrastructure management
- **Target Customers**: B2B/Enterprise – owners and operators of essential services in water, power, gas compression, pest control, HVAC, and manufacturing
- **Mission/Purpose**: “Beyond human capacity—we take advanced AI and autonomous technology and make the invisible state of physical infrastructure visible and actionable.”

## Products & Services
- **[Stateful Platform](https://bright.ai/)**: An edge AI platform that ingests sensor data and existing operational records to provide continuous monitoring, root-cause diagnosis, automated dispatch instructions, and machine learning. Deployed across physical assets and environments.
- **Workforce Wearables & Copilots**: Real-time AI-powered insights, communication, and operational support for field workers.
- **Asset & Site Visibility**: Real-time monitoring and proactive asset management for long-term system reliability.
- **Autonomous Inspection**: Safe, reliable data collection from remote and hard-to-access sites using robots and drones.

## Market Standing
- **Valuation**: Not publicly disclosed
- **Key Metrics**:
  - **Annual Revenue**: $75M (estimated, per LinkedIn data)
  - **Total Funding**: $66M (Seed $15M in Nov 2024, Series A $51M in July 2025)
  - **Headcount**: 103 employees (+121.7% YoY)
  - **Deployments**: 250K+ Stateful AI endpoints in the field; 50K+ locations in operating environments
- **Notable Investors/Partners**:
  - Series A lead investors: Khosla Ventures, Inspired Capital
  - Partners: Industry leaders in infrastructure (specific names not disclosed)
- **Growth Signals**:
  - Surpassed $100M milestone (likely an operating or revenue metric) in mid-2025
  - Added senior leaders from Microsoft, Evernote, and Intel in June 2025
  - 15 active job openings; monthly job posting growth +114.3%, yearly +650%

## Competitive Advantages
- **Proactive vs. Reactive**: Shifts infrastructure management from emergency repairs to continuous, predictive AI-driven operations.
- **Full-Stack Physical AI**: Combines edge computing, computer vision, LLMs, time-series signal processing, and robotics into one platform.
- **Founder Expertise**: CEO Alex Hawkinson previously founded SmartThings (acquired by Samsung) and co-founded EfficientAI and OurSky.
- **Real-World Scale**: Proven across 50K+ harsh environments (pipelines, power poles, mining vehicles).

## Strategic Focus
- **Scale Edge AI Deployments**: Accelerate adoption of the Stateful Platform across more infrastructure sectors globally.
- **Expand Leadership & Talent**: Strengthen executive team with deep tech and IoT experience to support rapid growth.
- **Deepen Autonomous Capabilities**: Invest in computer vision, LLM/RAG, time-series signal processing, and robotics to further reduce human intervention in inspection and dispatch.

## Why Work Here
- **Culture**: Values “Curious, Relentless, Collaborative, Impactful.” Emphasis on hands-on problem solving, ownership, and leaving a legacy.
- **Work Model**: Distributed team across the US (San Francisco to New York); remote/hybrid policy not explicitly stated but likely flexible given nationwide team.
- **Engineering Environment**: Deep talent pool across AI, embedded systems, cloud, and robotics; engineers work on cutting-edge physical AI problems.
- **Growth Stage**: High-growth company with 121.7% headcount growth YoY and substantial funding, offering career acceleration.

## Sources
1. [BrightAI Homepage](https://bright.ai/)
2. [BrightAI About Us](https://bright.ai/about-us/)
3. [BrightAI Careers Page](https://bright.ai/careers/)
4. [BrightAI Job Board (Greenhouse)](http://job-boards.greenhouse.io/brightai)
5. [BrightAI LinkedIn Company Page](https://www.linkedin.com/company/brightai)

## Other roles at BrightAI Corporation

- [Senior AI Engineer (Edge Dialog Systems)](https://feeny.ai/job/senior-ai-engineer-edge-dialog-systems-brightai-corporation-palo-alto-bhqr0tbw9qgq) — Palo Alto, CA
- [Senior / Staff Engineer, Robotics](https://feeny.ai/job/senior-staff-engineer-robotics-brightai-corporation-palo-alto-6w2yd82qetnf) — Palo Alto, CA
- [Computer Vision Engineer](https://feeny.ai/job/computer-vision-engineer-brightai-corporation-palo-alto-g591gyvk3dcm) — Palo Alto, CA
- [Staff Product Manager](https://feeny.ai/job/staff-product-manager-brightai-corporation-palo-alto-skvg6872kq3x) — Palo Alto, CA
- [AI Engineer, Time-Series Signal Processing](https://feeny.ai/job/ai-engineer-time-series-signal-processing-brightai-corporation-palo-alto-w5rdhqg2t6sd) — Palo Alto, CA