ML Research Engineer - Audio

As a Machine Learning Research Engineer - Audio at Happyrobot, your primary responsibilities will include leading model training and fine-tuning for various components such as LLMs and voice transcribers. You'll also be tasked with deploying and scaling model inference to handle large volumes of calls efficiently. Essentially, you will be a crucial part of enhancing our voice AI technology, ensuring it remains robust and effective in real-world applications.

To be a successful Machine Learning Research Engineer - Audio at Happyrobot, a preferred qualification is a Ph.D. or equivalent level of research experience in deep learning or applied AI, particularly in audio. The ideal candidate should possess a strong understanding of AI model training, deployment, and scalability while having a passion for technology and innovation in the voice AI sector.

Happyrobot believes that voice will become a dominant interface for digital systems, significantly enhancing user interactions and automating tasks across various industries, particularly logistics. As a Machine Learning Research Engineer - Audio, your role will contribute to making this vision a reality, building smarter AI agents capable of handling complex phone interactions.

In the role of Machine Learning Research Engineer - Audio at Happyrobot, you will engage in projects that involve optimizing the voice AI stack, conducting research experiments, and improving the efficiency of AI agents handling sales and customer service calls in the logistics sector. Each project aims to push the boundaries of what voice AI can achieve.

While the specific job listing for the Machine Learning Research Engineer - Audio position at Happyrobot does not mention a location, the company promotes a flexible working culture. You may have the opportunity to work remotely, making it easier to collaborate with the team while contributing to important projects in AI technology.

At Happyrobot, ownership is crucial for the Machine Learning Research Engineer - Audio role because it fosters innovation and accountability. By taking ownership, you can drive your projects, experiment with new approaches, and contribute meaningfully to the company's mission of enhancing voice AI technology. It encourages a proactive mindset, essential for thriving in a fast-paced startup environment.

As a Machine Learning Research Engineer - Audio at Happyrobot, familiarity with deep learning frameworks, audio processing tools, and natural language processing technologies is vital. Knowledge of large language models (LLMs), voice synthesis, and transcription systems will significantly enhance your ability to innovate and succeed in this role.

When answering this question, be specific about the models you've trained, the datasets you used, and the results you achieved. Highlight any unique challenges you faced and how you overcame them, providing concrete examples to illustrate your expertise and problem-solving skills.

Describe your methodology for optimizing models tailored for voice AI. Discuss techniques you've used, such as tuning hyperparameters, refining data preprocessing, or applying techniques like transfer learning to enhance model performance in real-time applications.

Provide details about any experiences you have in deploying models into production environments, particularly regarding how you ensured scalability and efficiency. Mention the tools or platforms you used and the metrics you monitored to ensure performance.

Explain the measures and methodologies you would implement to test and validate the robustness of a voice AI agent. This could include unit testing, integration testing, performance benchmarks, and handling edge cases to ensure a seamless user experience.

Discuss your strategies for staying informed about developments in AI research, such as attending conferences, participating in online courses, or engaging with academic journals and communities. This shows your commitment to continuous learning and growth in the field.

Use this opportunity to share a specific project and discuss the constraints you faced and the creative solutions you implemented. Highlight your ability to think critically and act decisively while maintaining quality and performance under pressure.

Discuss metrics such as accuracy, latency, user satisfaction, and cost-effectiveness. Explain how these metrics can vary based on use cases and how you would gather data and analyze these metrics post-deployment to drive improvements.

Share your approach to receiving feedback constructively and how you integrate it into the development process. Highlight your collaboration skills and how you ensure that all stakeholders are aligned with project objectives.

Emphasize the importance of collaborating with other teams, such as product management, UX design, and data engineering. Provide examples of how you have effectively worked in cross-functional teams to achieve project goals and drive innovation.

Describe your experience with audio data preprocessing, augmentation techniques, and any relevant tools you've utilized. Discuss specific projects where you transformed audio data into suitable formats for machine learning applications, showcasing your technical skills and conceptual understanding.