How You’ll Contribute:

As Staff Software Engineer on the ML Platform Team, you will work with the CTO to elevate our machine learning platform and extend our state-of-the-art text-to-speech models. This work includes optimizing our training pipeline, automating data processing, and redesigning abstraction layers.

You would be joining and leading a pioneering team with years of experience being the industry leader in text-to-speech.

In your day-today, you'll work on:

• Iteratively and incrementally improving our TTS service's ease of use, reliability, and performance.
• Building automated training and data processing services.
• Scaling our platform to load, process and validate tens of thousands of hours of text and speech data.
• Improving the performance and scalability of our machine learning training pipeline by integrating tools like DeepSpeed, PyTorch AMP, PyTorch JIT, and PyTorch Profiler.
• Researching techniques for better fine tuning and quantizing our models.
• Implementing processes to build confidence in our platform and services.
• Surfacing and leading discussions on the AI ethics and the social impact of your work at WellSaid Labs.
• Fostering and inclusive team culture and working environment.
• Open-source reusable libraries and tooling.

What We’re Looking For

To thrive in this role, you ideally have developed complex PyTorch models, experience deploying and monitoring models in production, and an deep understanding of scaling machine learning services. You ideally have led significant deep learning projects to address customer problems.

Ideally, you also have some combination of the following:

• Experience leading the development of a mature production machine-learning service.
• Has deployed deep learning models with Pytorch in the past 2 years.
• You have built and deployed ML models for use by a non-technical audience, clearly communicating usage guidelines and best practices.
• Great attention to detail.
• Affinity for creating modular, scalable, secure, and well-tested code.
• You deeply understand deep learning RND workflows, practices, and techniques.
• A background using data structures and algorithms to process large amounts of data.
• Experience implementing automating model training and data processing.
• Experience implementing various tooling for speeding up training.
• Strong cross-team collaboration skills.
• An agile, iterative mentality and approach to problem-solving.
• (Bonus) Experience building highly optimized layers (with C++, CUDA, etc.)
• (Bonus) Experience profiling and optimizing deep neural network performance.
• (Bonus) Experience scaling models past a billion parameters.

To join our team you must also:

• be a U.S. Citizen or Permanent Resident
• pass a pre-employment background check

What We Offer

WSL is proud to support an inclusive work environment that emphasizes each team member’s personal and professional growth. Our team is fully distributed throughout the U.S., and we support flexible schedules - work where and when you work best. You’ll have teammates just a Slack message or video call away if you ever need help solving an exciting challenge, or even if you just have a funny story to tell.

Other perks and benefits:

• Competitive salary and stock options
• Full medical, dental, and vision insurance
• Matching 401(k) plan
• Generous vacation policy/paid time off
• Parental leave
• Learning & development stipend
• Home office stipend

As a startup, we strive to be externally competitive with companies at a similar size and stage, and internally fair in our pay practices. The hiring salary range for this role is $180-210k and represents the target offer range given the scope and experience expectations for this role.

What to Expect From Us

We strongly encourage you to apply! If we feel your skills, experience, and values match, we’ll reach out about meeting with the team.

During the interview stage, you can expect:

• An introductory interview with the hiring manager (50 minutes); if there’s a match we’ll schedule an interview loop with the team.
• A take-home assessment which involves implementing small suggested improvements from research papers in PyTorch
• An Interview loop with 3-4 interviews (1 hour each) with the team members you will be potentially working with

All interviews will be remote via Google Meets; we are happy to make accommodations you might need to feel comfortable and set up for success in our process.