Senior ML Engineer – Speech/Audio (TTS / STT)

As a Senior ML Engineer – Speech/Audio at Synthflow.ai, your main responsibilities will include architecting and developing state-of-the-art machine learning models for TTS and STT applications. You'll lead training, fine-tuning, and optimization efforts to ensure our speech models achieve exceptional performance, while collaborating with cross-functional teams to ensure these technologies are effectively integrated into our products.

To qualify for the Senior ML Engineer position at Synthflow.ai, you should have a Master's degree in Computer Science, Electrical Engineering, or a related field. Additionally, a solid background in speech technology with over 3 years of experience in developing TTS and STT systems is essential, alongside strong Python programming skills and familiarity with modern machine learning frameworks.

Success as a Senior ML Engineer at Synthflow.ai hinges on a robust understanding of speech technologies, particularly TTS and STT. You should possess strong software development skills, experience with machine learning model implementation, and the ability to analyze and solve complex challenges in a dynamic environment. Collaboration with team members is also key, along with a continual interest in the latest industry trends.

The team culture at Synthflow.ai is centered around collaboration, respect, and innovation. We value input from every team member and encourage autonomy in shaping innovative projects. As a Senior ML Engineer, you will be part of a supportive environment that recognizes how individual contributions drive our collective success.

Synthflow.ai offers a comprehensive compensation package that includes competitive salary and stock options, promoting a sense of ownership and shared success. Furthermore, our commitment to a remote-first approach means you have the flexibility to work from anywhere while being a critical part of pioneering advancements in the AI no-code space.

When answering this question, provide specific examples of TTS and STT projects you've worked on. Highlight the technologies you employed, the challenges you faced, and how you overcame them. This showcases your hands-on experience and problem-solving skills in the field.

Discuss the methodologies you typically use, such as data preprocessing, feature extraction, and model evaluation. Share any specific frameworks or tools you prefer for training ML models, and explain how you measure their performance to ensure they meet desired standards.

Mention frameworks like TensorFlow, PyTorch, or Keras. Give insights into projects where you've used them, focusing on how these frameworks helped you solve specific challenges in your work with speech technologies.

Explain how you keep up with the latest research in AI, such as following relevant journals, attending conferences, or participating in online courses. Sharing a few key resources or recent technologies you've explored can demonstrate your proactive engagement with the industry.

Choose a relevant example from your experience, detailing the problem, your thought process, the solution you implemented, and the outcome. This illustrates your analytical and critical thinking abilities.

Emphasize the importance of collaboration in achieving project goals, the benefits of diverse perspectives, and share examples of how you successfully communicated with team members from various disciplines like NLP researchers and software engineers.

Talk about the metrics you use, such as accuracy, precision, recall, or F1 score, and the significance of each. Share your approach to validating models and how you iterate to improve performance based on these evaluations.

Demonstrate your knowledge of Synthflow.ai's projects and express genuine enthusiasm for specific initiatives. Discuss how your skills can contribute to these endeavors, creating a connection between your experience and the company's goals.

Discuss relevant coursework, projects, or research experiences during your education that specifically relate to TTS and STT technologies. Show how this academic background has equipped you with foundational knowledge and problem-solving skills for this role.

Share your methodologies for task prioritization, time management, and collaboration with team members. Mention any tools or frameworks you utilize to ensure projects are completed on time while maintaining high quality.