Senior ML Engineer - Speech/ASR (m/f/d)

As a Senior ML Engineer - Speech/ASR at voize, your key responsibilities will include designing, training, evaluating, and deploying advanced deep learning models focused on speech recognition. You'll work closely with user feedback to create products that truly meet the needs of healthcare professionals. Your role demands ownership of the machine learning process from beginning to end, ensuring that the models you develop are fine-tuned for real-world applications while maintaining focus on quality and efficiency.

To apply for the Senior ML Engineer - Speech/ASR role at voize, you're expected to have several years of hands-on experience with deep learning techniques specifically related to speech recognition. Strong expertise in STT system development, proficiency in Python and PyTorch, and a comprehensive understanding of the ML workflow— from conception to deployment—are essential. Experience in building and fine-tuning ASR systems, as well as familiarity with maintaining and optimizing ML models, will be highly valued.

At voize, we understand that a healthy work-life balance is crucial for productivity and overall job satisfaction. As a Senior ML Engineer - Speech/ASR, you will enjoy flexible working hours that allow you to work when you are most efficient. Our Berlin office provides a collaborative environment, but you also have the option to work remotely. Plus, with 30 days of vacation and your birthday off, we ensure you have ample time to recharge.

The team environment at voize is open, collaborative, and dynamic. As a Senior ML Engineer - Speech/ASR, you will work alongside passionate individuals who share a commitment to improving the healthcare system through technology. Regular team events and work weeks foster strong connections, while our culture encourages giving and receiving constructive feedback. You’ll find that challenges are viewed as opportunities, making every day an engaging experience.

At voize, you will find numerous opportunities for professional growth and personal development. As part of a fast-growing startup, the challenges you encounter will serve as valuable learning experiences. We also provide access to various learning platforms, including Blinkist and Audible, to encourage continuous learning. Additionally, by becoming a co-creator of our success through virtual stock options, you will have a stake in our growth as a company.

It's beneficial to provide specific examples of projects where you've developed or optimized speech recognition models. Discuss the challenges you faced, the techniques you used, and the outcomes of your work. Highlight any practical applications that demonstrate your ability to apply theory to real-world scenarios.

To answer effectively, describe the steps you take for model deployment, including testing, integration, and monitoring. Emphasize the importance of ensuring that models function correctly in real-time settings and discuss any specific tools or methodologies you've used to facilitate smooth deployment.

Discuss the factors that are crucial when fine-tuning an ASR model, such as data quality, feature selection, training parameters, and evaluation metrics. Explain your methodology for determining whether fine-tuning or building a model from scratch is more appropriate based on the project requirements.

Share the resources you rely on, such as research papers, industry journals, online courses, or conferences. Explain how you apply new knowledge to your work and your commitment to lifelong learning in this fast-evolving field.

Walk the interviewer through a specific situation where you encountered a problem with a model. Discuss your diagnostic process, the steps you took to identify the issue, and the resolution strategies you employed. Highlight your analytical skills and perseverance in solving complex challenges.

Discuss the various strategies you've implemented for optimizing models, including hyperparameter tuning, leveraging data augmentation techniques, and employing various architectures. Be specific about the results you've achieved through these optimizations.

Explain how you actively seek feedback from peers, users, or stakeholders. Share examples of times when feedback led to meaningful improvements in your projects, emphasizing your adaptability and openness to learning from others.

To ensure effective communication within your team, describe your strategies for collaboration, such as regular check-ins, using collaborative tools, and encouraging open dialogue. Stress the importance of fostering a team culture where everyone feels valued and heard.

Share your personal motivation tied to the mission of improving healthcare through AI. Discuss any experiences you've had or values that resonate with you, highlighting your commitment to making a positive impact through your work.

Discuss your process for learning new techniques, whether it be through online courses, hands-on experimentation, or collaborative projects. Emphasize your proactive nature in expanding your skill set to stay relevant in the rapidly changing machine learning landscape.