Machine Learning Engineer (Audio)

As a Machine Learning Engineer (Audio) at Zyphra, your responsibilities encompass a broad spectrum of tasks including conducting large-scale audio training runs, collecting and processing audio datasets, and optimizing performance within our training stack. You'll also design innovative training methodologies and contribute to architecture improvements, enabling the delivery of cutting-edge text-to-speech and audio models.

To excel as a Machine Learning Engineer (Audio) at Zyphra, candidates should possess a strong research mentality, excellent communication skills, and proficiency with PyTorch and Python. Experience in audio domains such as text-to-speech or speech emotion recognition is beneficial. A postgraduate degree in a relevant field, like Computer Science, is also advantageous for this position.

At Zyphra, the work environment for a Machine Learning Engineer (Audio) is collaborative and fast-paced, where both research excellence and engineering capabilities are highly valued. The team is passionate about exploring innovative ideas and typical activities include brainstorming sessions, collective problem-solving, and enjoying a culture that celebrates creative thinking.

Zyphra is committed to the professional growth of its Machine Learning Engineers (Audio) by fostering an environment of learning. You will have opportunities to work on ambitious projects that encourage you to quickly learn and implement new methodologies, as well as access to mentoring from experienced peers in a supportive environment, ensuring continuous skill development.

Zyphra offers an attractive benefits package for its Machine Learning Engineers (Audio), including medical, dental, and vision plans, competitive salaries, and equity options. Additional perks include on-site meals, relocation assistance as needed, and an engaging office culture that enriches your overall work experience.

In answering this question, focus on any relevant projects or research you've conducted involving audio models, especially in areas like text-to-speech or ASR. Provide specific examples of technologies or frameworks you've used, underlining your hands-on experience and ability to engage deeply with audio processing tasks.

Explain your approach to experimental design, covering aspects like hypothesis formulation, data collection, model training, and performance evaluation. Highlight your understanding of running rigorous ablation studies and how you apply careful experimental methodology to enhance model performance effectively.

Discuss the problem-solving strategies you employ when faced with challenges. Emphasize collaboration, your inclination to research potential solutions, and how you've previously adopted new techniques to overcome barriers in optimizing models, illustrating your ability to adapt and innovate under pressure.

When responding, mention your proficiency in Python and specifically PyTorch as required by Zyphra. Include any additional languages or tools you're comfortable with, explaining how you've leveraged them in previous projects to facilitate efficient model development and data analysis.

Share your strategies for staying current with trends and research in machine learning and audio processing, such as following influential publications, participating in workshops, and engaging with online communities or forums. This showcases your dedication to lifelong learning and passion for the field.

Be specific about your role in the project, detailing the model's objectives, the challenges faced, and your contributions to its development. Highlight how your efforts led to measurable outcomes, demonstrating your impact within a team setting and your capacity for taking initiative.

Illustrate your teamwork skills by sharing examples of how you've successfully collaborated on projects, communicated effectively, and resolved conflicts. Emphasize the importance of open communication and leveraging team strengths to achieve collective goals in a dynamic workspace.

Stress the significance of experimentation in developing, refining, and validating models. Describe how you approach different experimentation phases, including setting up tests, analyzing results, and iterating on designs based on feedback and performance measurement.

Discuss your experience with data preprocessing, data augmentation, or any techniques you've utilized to prepare data for training machine learning models. Highlight specific tools or libraries and how you've ensured data quality and relevance for the task at hand.

Express your passion for audio machine learning by discussing specific aspects you find compelling, whether it's the challenge of developing natural-sounding speech models or the potential for innovation in audio-driven applications. Share your vision for future advancements in the field and how you hope to contribute.