Generative AI - WFH

# Applied Scientist, Generative AI / Machine Learning

## About the Role

Figma is looking for Applied Scientists with a Machine Learning (ML) and Artificial Intelligence (AI) background to build AI technologies that improve Figma products. In this role, you will drive fundamental and applied AI research, explore the boundaries of current technology, and design and build AI/ML models and systems using industry best practices and first-principles approaches.

You will work closely with product managers, product engineers, infrastructure engineers, and other teams to develop AI-powered features, translate user feedback into AI system requirements, and build evaluation systems that measure and improve AI quality. This is a full-time role that can be based at one of Figma’s US hubs or remotely within the United States.

## Key Responsibilities

* Drive fundamental and applied research in AI and explore new approaches for building models for Figma’s domains.

* Design and build Generative AI models using techniques including Supervised Fine-Tuning (SFT), Reinforcement Learning (RL), prompt improvement, and synthetic data generation.

* Apply industry best practices and first-principles approaches to AI/ML model and system development.

* Work with product and infrastructure engineers to improve Figma products through AI-powered features.

* Collaborate with product managers and engineers to translate user feedback into requirements for AI systems.

* Build evaluation systems to measure and improve the quality of AI features in Figma products.

* Develop and deploy Generative AI features through prompt engineering and model fine-tuning in production environments.

## Required Qualifications

* Extensive experience building Generative AI features through prompt engineering and fine-tuning models in production environments.

* Experience with deep learning and Generative AI frameworks such as PyTorch, JAX, and Hugging Face.

* Experience training Large Language Models (LLMs) using Reinforcement Learning techniques such as preference-based RL, including DPO and PPO, and/or RL with verifiable rewards (RLVR), including GRPO and DAPO.

* 4+ years of experience in Generative AI.

* 6+ years of experience in one or more of the following areas:

* Machine Learning

* Natural Language Processing/Natural Language Understanding (NLP/NLU)

* Computer Vision

* 5+ years of programming experience using one or more of the following languages:

* Python

* C++

* Java

* R

* Strong software engineering skills.

* Experience communicating and working across functions to drive solutions.

## Preferred Qualifications

* Experience with RL from execution feedback for coding.

* Experience with Reinforcement Learning involving non-verifiable rewards.

* Proven track record of planning multi-year roadmaps where shorter-term projects contribute to a long-term vision.

* Experience mentoring or influencing senior engineers across organizations.

* Expertise in large-scale and distributed AI training.

## Skills & Competencies

* Generative AI

* Machine Learning

* Artificial Intelligence

* Deep Learning

* Large Language Models (LLMs)

* Reinforcement Learning (RL)

* Preference-Based Reinforcement Learning

* RL with Verifiable Rewards (RLVR)

* Prompt Engineering

* Model Fine-Tuning

* Synthetic Data Generation

* AI Model Evaluation

* Natural Language Processing / Natural Language Understanding

* Computer Vision

* Software Engineering

* Python, C++, Java, or R

* Cross-Functional Collaboration

* Communication

* Product and User Feedback Translation

## Education & Experience

**Education:**

No specific educational degree requirement is stated in the original job description.

**Experience:**

* 4+ years of experience in Generative AI.

* 6+ years of experience in Machine Learning, Natural Language Processing/Natural Language Understanding, Computer Vision, or a combination of these areas.

* 5+ years of programming experience with Python, C++, Java, or R.

* Experience building Generative AI features and fine-tuning models in production environments.

## Work Arrangement & Schedule

* **Employment Type:** Full-time

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