Applied AI Engineer, Video Agent
AI in this role
Applied AI Engineer building multi-step video generation AI agents, eval frameworks, and full-stack product features.
About HeyGen
At HeyGen, our mission is to make visual storytelling accessible to all. Over the last decade, visual content has become the preferred method of information creation, consumption, and retention. But the ability to create such content, in particular videos, continues to be costly and challenging to scale. Our ambition is to build technology that equips more people with the power to reach, captivate, and inspire audiences.
Learn more at www.heygen.com. Visit our Mission and Culture doc here.
About the Video Agent team
Video Agent turns a prompt, a document, or a handful of assets into a finished video. Behind a simple chat box sits a multi-step AI agent. It plans a storyboard, writes the script, chooses avatars, voices, and visuals, calls many tools along the way, and hands the result to our rendering pipeline. Customers use it every day to make training videos, product explainers, marketing content, and more.
We are a small team that owns the agent end to end. That covers the prompts and tools the agent uses, the backend services that run it, and the product screens where people chat with it and edit what it makes.
This is not a role where you only call someone else’s model through an API. HeyGen trains its own models in house, and the agent is built on top of them. You will work directly with those models and with the researchers who build them, which is an experience few applied AI roles offer.
About the role
You will make Video Agent better at doing what the user actually asked for, and you will prove it with data. Some weeks that means redesigning a tool the agent calls. Other weeks it means building an eval, running an A/B test, or shipping a new editing experience in React. You will own features from the first idea through launch and follow-up, and you will decide which problems belong to the model and which belong to plain, deterministic code.
What you’ll do
- Build the agent. Design the tools, prompts, and context the agent works with. Improve how it plans, how it recovers from mistakes, and how it handles long, multi-turn conversations.
- Measure quality. Build evals and offline replays of real sessions. Run experiments behind feature flags and read the results honestly. Watch real user sessions and agent traces to find where things go wrong.
- Ship across the stack. Build user-facing features in TypeScript and React. Build backend services and long-running workflows in Python and Go.
- Work closely with others. Partner with product managers, designers, and the research and rendering teams that the agent depends on.
What we’re looking for
- You have built and shipped an AI agent that real people use. We mean a system where a model calls tools, plans across several steps, and acts on the results. A chat wrapper or a prompt demo is not enough on its own. We want to hear what broke in production and how you fixed it.
- You know how to tell whether an AI system got better. You have built evals or test sets for model behavior. You can tell a real improvement from noise, and you are skeptical of results that look too good.
- You are a strong full-stack engineer. You have 2 or more years of professional experience. You are fluent in TypeScript and React on the frontend and Python on the backend.
- You have strong product sense. You use what you build, you care how it feels, and you look at how customers actually use it.
- You take ownership. You do not wait for a spec. You find the most important problem, fix it, and tell people what changed.
A degree in computer science or a related field is welcome but not required. Equivalent experience counts.
Nice to have
- You have fine-tuned models before.
- You have worked on code-to-video models, which generate video by writing code such as HTML or animation scripts.
- You have worked on video agent models in general.
Show us an agent you’ve built
With your application, please include a short note (a few sentences is fine) about an agent you have built. Tell us what it does, how you measured whether it worked, and one failure you found and fixed. A link to code, a demo, or a write-up is a bonus.
Compensation & Benefits
- $180,000 – $240,000 annually
- Equity in a high-growth AI company.
- 401K Match
- Health, dental, and vision benefits.
- Flexible PTO.
- Opportunity to help define a new category in AI video.
- High ownership, high velocity, and direct impact on revenue growth.
Equal Opportunity
HeyGen is an Equal Opportunity Employer. We value diversity and are committed to building an inclusive environment for all employees.
How we rate this
Applied AI Engineer, Video Agent at HeyGen rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- How do you decide when an AI agent can act on its own versus asking for approval first?
- Tell me about a project where applied ai was part of your work. What did you do?
- Tell me about a project where machine learning was part of your work. What did you do?
- Tell me about a project where full stack was part of your work. What did you do?
- Walk me through how you've used Python in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: AI Agents, Applied AI, Machine Learning, Full Stack, and Python. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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