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Open nowPosted 5 days ago

Solution Engineer

Embedding VC57 open roles

Where
Mountain View, CA
Work mode
Hybrid
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Your applicationOpen nowSolution EngineerEmbedding VC · Mountain View, CA

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

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The clock on this job

Early applications get read.

13.8% of postings close within 7 days. Measured by our own scanner across the market. Embedding VC postings stay open a median of 20 days.

Share of postings closed within
  1. 2.2%1 day
  2. 4.7%3 days
  3. 13.8%7 days
  4. 22.3%14 days
  5. 37.8%30 days
  6. 59.0%60 days
This job: posted 5 days ago

Embedding VC median: 20 days open

The posting

This Isn't Your Typical Engineering Job

Imagine this: you're backstage at the NFL Super Bowl, making sure a physical AI agent is ready to go live in front of millions — and NFL Commissioner Roger Goodell walks by to check it out. A few weeks later, you're at San Jose International Airport deploying AI alongside Mayor Matt Mahan. Then you're at NVIDIA GTC, the world's biggest AI conference, and you bump into Jensen Huang himself.

That's not a hypothetical. That's what our team actually did.

At LiveX AI, we build physical AI agents — intelligent, embodied systems that interact with people in the real world. Not chatbots on a screen. Real AI that lives in airports, stadiums, conference halls, and beyond. We're looking for a bold, driven Solution Engineer to join our core team full-time and help us deploy these systems on the world stage.

About LiveX AI

LiveX AI is a fast-growing startup headquartered in the San Francisco Bay Area, building the next generation of physical AI agents. Our technology combines advanced large language models, multimodal AI, and purpose-built hardware to create AI experiences that people interact with face-to-face. We're backed by leading investors and trusted by enterprises, cities, and major event organizers worldwide.

What You'll Do

You'll be embedded in our core deployment team — the people who take our AI from the lab to the real world.

- Ship physical AI agents. Build, test, and deploy AI-powered hardware systems to real venues — airports, stadiums, conference centers, retail spaces. You'll own deployments end to end and see your work in action, not just in a staging environment.

- Get your hands dirty with hardware. Set up screens, sensors, networking gear, and edge compute devices. Debug on-site. Make AI work in the physical world, where Wi-Fi drops, power flickers, and thousands of people walk by.

- Write real code. Build software that powers our AI agents — Python, TypeScript, APIs, integrations. Contribute to the same codebase the rest of engineering works in. Your commits go to production.

- Work the world's biggest stages. Lead and support deployments at marquee events and high-profile locations. Previous team deployments include the NFL Super Bowl, San Jose Airport, and NVIDIA GTC. You could be next.

- Talk to real humans. Present demos to customers, partners, and VIPs. Explain how our AI works to both technical and non-technical audiences. Your communication skills matter as much as your code.

- Shape the product. Feed real-world observations back to engineering and product. When you notice something in the field, your input directly influences what we build next.

How We Evaluate Candidates

We hire for the traits that make someone thrive in fast-moving, high-stakes deployments. Every candidate is evaluated on four things:

- Responsibility & dedication. You take full ownership of your work and follow through. When a deployment is on the line, you do whatever it takes to get it right — and you can be counted on.

- Speed of learning. Our stack, our hardware, and our customers change fast. You pick up new tools, technologies, and contexts quickly and turn them into results without hand-holding.

- Multi-tasking capability. You juggle code, hardware, customers, and live events at once — often in the same day — and keep all of them moving without dropping the ball.

- Communication skills. You explain technical concepts clearly to anyone — a mayor, a CEO, a teammate, or a curious bystander — and you keep the team aligned under pressure.

- Who You Are

- Ideally, you've delivered enterprise software at a startup, or run enterprise projects at a consulting firm — you know how to ship for demanding customers and manage delivery end to end.

- You code. Python, JavaScript/TypeScript, or both. You've built and shipped things that went beyond the spec.

- You're genuinely excited about AI — not just the hype, but how LLMs, computer vision, and multimodal models actually work under the hood.

- Hardware doesn't scare you (or it does, but you want to learn anyway). You're willing to carry a monitor, run cables, and troubleshoot on-site.

- You communicate clearly — to a mayor, a CEO, or a curious bystander at a trade show.

- You thrive in chaos. Startups are messy. Deployments have surprises. You stay calm, figure it out, and keep going.

- You're a team player who brings energy, not ego.

Bonus Points

- Experience with LLM frameworks (LangChain, LlamaIndex), prompt engineering, or RAG pipelines.

- Familiarity with edge computing, IoT, AV systems, or robotics.

- Prior startup or hackathon experience where you shipped something under pressure.

- Background in CS, EE, information systems, or a related technical field.

Why You'll Love This

- World-stage experiences. Your LinkedIn won't say "updated Jira tickets." It'll say "deployed AI at the Super Bowl."

- Full-stack, full-real. AI + software + hardware + customer-facing — you'll build a skill set most engineers don't reach until years into their careers.

- Silicon Valley, front row. You'll work at the center of the physical AI movement, with the connections and momentum that come with it.

- Mentorship that matters. You'll work directly with founders and senior engineers who want to see you grow — not hand you busywork.

- Real ownership & growth. Top performers take on more scope fast — leading deployments, owning customers, and shaping the product.

- Competitive salary, equity, and benefits.

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