Senior ML Engineer/Researcher

As a Senior ML Engineer/Researcher at PlayAI, you'll be responsible for designing and building large-scale data pipelines, experimenting with and enhancing our Voice LLM architectures to improve quality and latency, and optimizing inference infrastructure to reduce costs. Additionally, you'll play a key role in scaling and optimizing our distributed training infrastructure, which is crucial to our innovative Conversational AI products.

To qualify for the Senior ML Engineer/Researcher position at PlayAI, candidates should have a proven track record in deep learning, showcasing an autonomous and resourceful approach to problem-solving. Hands-on experience in deep learning and distributed training of LLMs, Generative Models, and Transformers is essential. A solid foundation in programming languages like Pytorch, Python, and C++ is also a must. Familiarity with Speech Synthesis is a significant bonus!

PlayAI fosters an autonomous working environment for its employees, especially for the Senior ML Engineer/Researcher role. You can expect to tackle challenging problems with the freedom to explore and innovate. Our startup culture encourages creativity and collaboration while supporting individual growth and contributions that significantly impact our mission of advancing Conversational AI.

PlayAI offers a competitive compensation package that includes equity, health, dental, and vision insurance, as well as commuter benefits. You will also enjoy flexible work hours and a generous PTO policy, which allows for a healthy work-life balance. This is a fantastic opportunity to be part of a rapidly growing startup that values its employees and encourages autonomy.

Since joining YC W23, PlayAI has achieved tremendous growth, securing $20 million in seed funding and expanding its user and revenue base by 20 times in just two years. This impressive growth trajectory means that as a Senior ML Engineer/Researcher, you'll be joining a thriving company with ample opportunities for professional development and the chance to significantly impact the company's future and the world of Conversational AI.

When answering this question, highlight specific projects where you've utilized Pytorch, focusing on the types of models you built and how you implemented them. Discuss your familiarity with the framework's features such as dynamic computation graphs and its ecosystem, showcasing your hands-on experience and technical proficiency.

When discussing your approach to optimizing data pipelines, emphasize your ability to analyze performance bottlenecks and your experience with data preprocessing, feature engineering, and efficient data storage solutions. Provide examples of tools or technologies you have used to improve speed and reliability in data processing.

Share specific techniques you've implemented or explored to enhance LLM architectures, such as attention mechanisms, layer normalization, or innovative training schedules. It's important to relate these techniques back to results you've achieved, like improved performance metrics or reduced latency in real-world applications.

In your response, explain your understanding of distributed training concepts, the frameworks you've used (like TensorFlow or PyTorch), and the benefits it provides. Discuss specific projects where you've applied these concepts and the outcomes, such as reducing training time or improving model accuracy.

Discuss your method for validating models, including cross-validation, hyperparameter tuning, and ensuring they meet desired performance metrics. Providing concrete examples of how you measure and improve model accuracy will make your answer stronger.

Share specific examples of challenges you've encountered, such as model drift or latency issues, and discuss the strategies you've employed to address these challenges. Highlighting your problem-solving skills and ability to adapt will demonstrate your readiness for the Senior ML Engineer/Researcher role.

Talk about the resources you utilize to stay informed, such as attending conferences, reading journals, following key influencers in the field on social media, or participating in relevant online courses. This demonstrates your commitment to continuous learning and staying ahead in the field.

Provide a clear explanation of transfer learning, elucidating its benefits such as reducing training time, improving performance on smaller datasets, and leveraging knowledge from pre-trained models. Support your answer with examples from your experience where applicable.

Using the STAR (Situation, Task, Action, Result) technique, describe a collaborative project where each team member played a vital role. Highlight your contributions, how you facilitated communication, and the overall success of the project to emphasize your teamwork and leadership skills.

Share your passion for Conversational AI and its potential impact on various industries. Discuss specific aspects that excite you, such as improving user interactions or creating more human-like AI agents, and relate this to PlayAI’s mission to inspire enthusiasm for the role.