Staff Research Engineer, TTS

Remote - US

WellSaid Labs

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Who We Are: WellSaid Labs 

We’re creating a Voice for everyone.

At WellSaid Labs, we enable creatives around the globe by putting high-tech, human parity technology into their hands, giving them the ability to add voice-over to any project and iterate with ease. Creative teams use WellSaid Lab’s Voice Studio to create compelling employee training, design unique digital experiences, and narrate audiobooks. We believe deeply in AI for Good, and that technology should be empowering, engaging, and fair to all people

How You’ll Contribute:  

As a Staff Research Engineer on the ML Platform Team, you will work with the CTO and other ML Platform Team members to elevate our machine learning platform and extend our state-of-the-art text-to-speech models. This work includes optimizing our training pipeline, adding new features to our models and designing entirely new TTS models focused on specific areas such as speed or emotions. 

 

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

In your day-to-day, examples of what you'll work on include:

  • Staying at the edge with the latest advances in speech AI with a focus on applications within our products.
  • Researching techniques for better fine tuning and quantizing our models.
  • Researching and developing model architectures to address specific needs.
  • 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.
  • Documenting results and presenting complex technical concepts in a clear manner for multiple stakeholder audiences, including our customers.
  • Improving the performance and scalability of our machine learning training pipeline by integrating tools like DeepSpeed, PyTorch AMP, PyTorch JIT, and PyTorch Profiler.
  • 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 with large datasets from start to finish, including model evaluation and acceptance.  You have experience working with a variety of stakeholders as well as experience working with audio data in the context of machine learning, such as ASR or TTS.  You ideally have been a thought leader in 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.
  • 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 R&D 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 iterative mentality and approach to problem-solving, with a change mindset that can adapt to evolving product requirements.
  • (Bonus) Experience building highly optimized layers (with C++, Rust, 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 working with audio in PyTorch
  • A follow-up interview loop that builds on the take-home assessment and involves a live coding exercise, as well as time to speak 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.

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* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰

Tags: Architecture ASR CUDA Deep Learning Machine Learning ML models Model training Open Source PyTorch R R&D Research Rust

Perks/benefits: 401(k) matching Career development Competitive pay Equity Flex hours Flex vacation Health care Home office stipend Medical leave Parental leave Salary bonus Startup environment Team events

Regions: Remote/Anywhere North America
Country: United States
Job stats:  20  1  0

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