What are the responsibilities and job description for the Junior Machine Learning Engineer/Researcher position at Cambio AI Inc.?
About Us :
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.
Find out if this opportunity is a good fit by reading all of the information that follows below.
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 last year 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 Junior 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 a founding 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.
Enhancing inference infrastructure to reduce costs and latency.
Scaling and optimizing LLM distributed training infrastructure.
Qualifications :
Previous academic or work experience in deep learning.
Demonstrates a growth mindset and a passion for solving challenging problems.
Possesses academic or previous work experience in deep learning and distributed training of LLMs, Generative Models, and Transformers.
Experience with Pytorch, Python, and C .
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 :
The chance to be a founding engineer in a rapidly growing startup.
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.
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