PlayAI (YC W23) is at the forefront of generative voice and conversational LLMs. With our Speech Synthesis and Voice Cloning models, we are building SOTA conversational AI products.

The mission is clear: we are building hyperrealistic conversational voice agents & voice AI infrastructure to enable every business, developer, and creator to build the most human-like automated experiences to ever exist. This will completely change the way we interact with technology.

The need and demand are clear. This past year, we have raised $20M+ in seed funding and have seen significant growth, with 1M monthly active users and 250K developers, with revenue growing 30% MoM.

PlayAI is at the forefront of generative voice and conversational LLMs. With our Speech Synthesis and Voice Cloning models, we are building the SOTA conversational AI products.

We are building a platform and infrastructure for Conversational AI Voice Agents so that every business, developer, or tinkerer can easily build talking human-like AI agents and use them to serve their customers; this will unlock massive value for the world and a lot of happiness for people using these delightful agents.

We joined YC in the YC W23 batch. Since then, we have raised $20m in seed funding and seen significant growth in users and revenue (20x the last two years).

What are we looking for?

We are in search of Machine Learning Engineers and Researchers who are passionate about solving challenging problems and inventing the future of how people interact with LLMs. By joining our team, you have the opportunity to be an early engineer and play a pivotal role in shaping the future of Conversational AI. If you're keen on pushing AI boundaries and making a significant impact, this role is for you.

Responsibilities:

• Designing and building large-scale data pipelines.

• Experimenting and improving our Voice LLMs architectures for better quality, expressiveness, and latency.

• Scaling and optimizing LLM distributed training infrastructure.

Qualifications:

• Demonstrates a growth mindset and a passion for solving challenging problems.

• Possesses previous academic or work experience in deep learning and distributed training of LLMs, Generative Models, and Transformers.

• Experience with Pytorch, Python (familiarity with other distributed training frameworks is a plus).

• Familiarity with Speech Synthesis is a significant plus.

• Experience with Direct Preference Optimization (DPO) and/or Reinforcement Learning from Human Feedback (RLHF) is a significant plus.

• Master's degree in a related technical field or Bachelor's degree from a top-tier university with relevant work experience (internships, full-time roles, or equivalent). Recent graduates and current students are encouraged to apply.

What We Offer:

• Challenging problems to solve.

• Autonomous working environment

• Competitive compensation

• Flexible work hours

• Health, dental, and vision insurance

• Commuter benefits

• Flexible PTO + holidays

Final offer amounts are determined by multiple factors, including experience, and may vary from the amounts listed above.