Staff/Principal Machine Learning Engineer

We are seeking Staff and Principal Machine Learning Speech Engineers with extensive experience in R&D of text-to-speech (TTS) and speech-to-text (STT) technologies. In this role, you will be at the forefront of building generative AI stack to power next-generation AI characters.

Minimum Qualifications

• Bachelor’s degree in Computer Science, Engineering, or a similar technical field.
• 6+ years of experience with software development in one or more programming languages, machine learning algorithms and tools (e.g., PyTorch), artificial intelligence, deep learning and/or natural language processing.
• Excellent problem solving skills and the ability to work independently and as part of a team.

Preferred Qualifications

• Master's degree or PhD in speech synthesis/recognition or adjacent fields.
• 5+ years of experience with design and architecture, and testing/launching software products.
• 1+ years of experience in working with sourcing and curating speech datasets.
• 1+ years of experience in a technical leadership role leading project teams and setting technical direction.
• 1+ years of experience in building end-to-end speech processing systems and real-time applications.

Responsibilities

• Research and experiment with cutting edge ML techniques for TTS and STT applications.
• Develop and test production-grade training and inference pipelines for TTS and STT applications.
• Understand optimization problems in the area of speech, signals, and natural language processing.
• Collaborate with cross-functional teams to integrate speech technologies into products.

In-office location: Mountain View, CA, United States.

Remote location: United States.

The US base salary range for this full-time position is $240,000 - $385,000. In addition to base pay, total compensation includes equity and benefits. Within the range, individual pay is determined by work location, level, and additional factors, including competencies, experience, and business needs. The base pay range is subject to change and may be modified in the future.

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