Job Url: https://jobs.ashbyhq.com/playai/c3c738a9-9e53-42c5-b049-84c9c89d642b

Job Description: ML Engineer/Researcher
Location
Palo Alto, CA

Employment Type
Full time

Location Type
On-site

Department
ML

Compensation
$100K – $160K • Offers Bonus
Overview
Application
PlayAI (fka PlayHT, YC '23) is at the forefront of generative voice and conversational LLMs, reshaping how humans interact with technology. Our advanced Speech Synthesis and Voice Cloning models power hyperrealistic, human-like conversational experiences across industries.

We’re building the core infrastructure for conversational AI—enabling businesses, developers, and creators to easily build intelligent voice agents and interactive voice applications. Whether it's serving customers or powering creative projects, PlayAI helps bring talking, human-like AI to life.

Since finishing Y Combinator’s W23 batch, we’ve raised over $21M in seed funding, grown to 1.4M+ monthly active users and 500K+ developers, and are scaling revenue at 35% quarter-over-quarter.

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.