GammaPosted 10mo ago
AI Engineer at Gamma scores 87 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.
AI in this role
You'll own the core AI systems that power Gamma: the models, prompts, and pipelines behind text, image, and layout generation. With over 1 million AI-generated presentations and 6 million AI images created every day, this work operates at massive scale. Your job is to elevate quality, evaluate new frontier models, and push into new capabilities and modalities.
This role is about productizing existing foundation models, not training new ones. You'll focus on prompting, evaluating, and fine-tuning for maximum performance across Gamma's product surface. You'll also launch new modalities like voice and video, build the evaluation infrastructure to measure what "good" looks like, and own uptime, latency, and cost across our AI stack. You'll work closely with engineering and product to ship improvements that millions of users feel immediately.
You'll thrive here if you're a tinkerer who loves pushing foundation models to their limits and you're equally comfortable writing prompts and writing production code. If you get excited about mixing prompt engineering with software engineering to unlock new AI capabilities, this is your role.
Our team has a strong in-office culture and works in person 4–5 days per week in San Francisco. We love working together to stay creative and connected, with flexibility to work from home when focus matters most.
What you'll do
Own Gamma's LLM and image prompts, measuring and continuously improving quality at scale across text, layout, and visual generation
Develop complex prompts for new features using AI JSX, balancing creativity with reliability
Build evaluation frameworks for prompts and models, combining quantitative metrics with qualitative feedback to create better test sets
Drive the AI roadmap based on quality gaps, constantly evaluating new frontier models and methods
Curate datasets for fine-tuning open-source models and launch new modalities like voice and video
Own analytics, tracking, uptime, latency, and cost across Gamma's AI infrastructure
What you'll bring
Proven track record pushing foundation models to their limits, with hands-on experience building and evaluating prompts at scale
Proficiency in TypeScript and Python, with a software engineering foundation that lets you move fluidly between prompt engineering and production development
Strong data instincts: experience writing evals, designing metrics, and translating qualitative feedback into measurable, actionable improvements
Self-sufficient in gathering and cleaning data to inform prompt improvements and model evaluations
Experience with modern LLMs and image models such as Flux and Imagen (Nice to have)
Familiarity with AI tooling like AI JSX for prompting and Braintrust for evaluations (Nice to have)
Compensation range:
The base salary for this full-time position, which spans multiple internal levels depending on qualifications, ranges between $180K - $300K plus benefits & equity.
Final offer amounts are determined by multiple factors, including but not limited to experience and expertise in the requirements listed above.
If you're interested in this role but you don't meet every requirement, we encourage you to apply anyway! We're always excited about meeting great people.
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 structure and test a prompt to get consistent output from a language model?
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
Adapt your resume
- List these exact terms on your resume: Prompt Engineering and Fine Tuning. 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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