ML Research Engineer - Audio

As an ML Research Engineer - Audio at Happyrobot, you will focus on optimizing our voice AI stack, applying the latest research to make our AI agents smart and efficient. Your responsibilities will include leading model training and fine-tuning, deploying scalable models, and contributing to daily product shipments, making a significant impact on how we handle communication in logistics.

To excel as an ML Research Engineer - Audio at Happyrobot, candidates should have a solid understanding of deep learning, experience in applied AI research specifically in audio, and preferably a PhD or equivalent experience. Independence, ownership, and a strong work ethic are also essential for this role.

While a PhD or equivalent level of research experience is preferred for the ML Research Engineer - Audio position at Happyrobot, we also value practical experience in deep learning and applied AI research in audio. If you possess a strong background and relevant skills, we encourage you to apply!

As an ML Research Engineer - Audio, you will work with cutting-edge technologies focusing on deep learning frameworks, large language models, audio transcription systems, and scalable model deployment tools. Your role will allow you to engage with the latest advancements in AI research that directly influence our products.

ML Research Engineers - Audio at Happyrobot face challenges such as enhancing the intelligence and robustness of voice AI agents, optimizing model performance for scale, and experimenting with novel algorithms to push the boundaries of what our AI can achieve in real-world applications. Your creativity and technical skills will play key roles in overcoming these challenges.

While the job location is not specified, Happyrobot embraces a flexible work culture that supports remote working options. This flexibility allows our talented team members to collaborate effectively no matter where they are, making it possible to contribute to our mission from the comfort of their own workspace.

The work culture at Happyrobot encourages innovation, independence, and ownership. As an ML Research Engineer - Audio, you will become part of a vibrant team that values hard work and collaborative efforts. We aim to create an environment where everyone can thrive through creativity and continuous learning.

Discuss your relevant projects, highlighting specific deep learning models you have implemented for audio tasks. Be prepared to mention the frameworks you used, the challenges you faced, and how you overcame them, demonstrating your hands-on expertise.

Share a structured overview of a machine learning project you led, including the problem statement, the dataset used, the algorithms selected, and the results achieved. Focus on lessons learned and how the project impacted users or the company.

Explain your methodology for fine-tuning, emphasizing the importance of understanding the dataset, adjusting hyperparameters, employing transfer learning, and optimizing for speed and accuracy. Cite specific techniques or metrics you prioritize in your process.

List tools and frameworks you're experienced with, such as TensorFlow, PyTorch, or specialized audio libraries. Provide examples of how you've utilized them in your past work, showcasing your technical depth and versatility in dealing with audio data.

Discuss your strategies for staying updated, such as following industry publications, attending conferences, participating in online courses, or engaging in research communities. Share specific resources or influential works that have shaped your understanding of audio ML.

Address potential issues such as model efficiency, resource constraints, maintaining accuracy under varied conditions, and the importance of robust error handling. Propose how you would tackle these challenges with teamwork and innovative solutions.

Highlight your ability to bridge research and practical application. Share a vision where your research informs product improvements, and vice versa, demonstrating enthusiasm for enhancing both the scientific and user experience aspects of AI technology.

Provide details on any projects involving LLMs, discussing how you trained or fine-tuned these models. Mention specific applications, outcomes, and your role in integrating these models within existing systems.

Illustrate your awareness of common communication hurdles faced in logistics, like efficiency, clarity, and timeliness. Share your thoughts on how AI audio agents can address these issues and improve overall operations.

Express your passion for advancing communication technologies and how audio AI can revolutionize industries. Share personal anecdotes or projects that inspired you, aligning your motivations with Happyrobot's mission and values.