As a Staff Applied Audio Research Engineer at Gladia, your main responsibilities include researching, training, and optimizing models for various audio processing tasks such as ASR and diarization. You will lead the end-to-end lifecycle of audio models and collaborate with different teams to develop scalable ML features. Additionally, you will benchmark state-of-the-art methods, improve system performance, and mentor other engineers while guiding strategic projects.

To apply for the Staff Applied Audio Research Engineer role at Gladia, candidates should have 8-10 years of experience in audio ML or deep learning engineering. Strong expertise in Python and a low-level programming language like C++, as well as hands-on experience with training and deploying audio deep learning models, are necessary. Familiarity with audio-specific architectures and experience in optimizing performance in production environments are also important qualifications for this position.

At Gladia, you can expect a collaborative and fast-paced startup environment that thrives on creativity and innovation. With full remote work options and team gatherings in Paris every 1.5 months, you will be part of a dynamic team where your input is valued. We offer a supportive culture that encourages pushing boundaries and solving complex challenges in the field of AI.

Gladia is committed to the professional growth of its Staff Applied Audio Research Engineers through mentorship opportunities and contributions to strategic projects. The role encourages continuous learning and keeps you up-to-date with the latest advancements in audio processing and deep learning technologies, fostering an environment where skills can be developed and honed.

Gladia provides an attractive package for its Staff Applied Audio Research Engineers, including a full remote work policy, generous lunch vouchers, a sports activity allowance, and health insurance coverage. Additionally, team gatherings in Paris and an unlimited vacation policy promote a healthy work-life balance, making Gladia an excellent place to advance your career.

When answering this question, highlight specific projects where you trained audio models, including the techniques you used and any challenges you faced during deployment. Discuss your familiarity with frameworks like ONNX or TensorRT and how they contributed to the model's performance in production settings.

Talk about the different architectures you've utilized, such as RNNT, Seq2Seq, or Conformers. Explain why you prefer certain architectures based on their strengths, such as latency or memory efficiency, and provide examples of projects where you've successfully applied them.

Share a specific challenge related to audio ML, detailing the problem, your approach to solving it, and the outcomes. Emphasize your problem-solving skills and critical thinking, demonstrating your ability to overcome challenges in a production environment.

Explain your approach to model optimization, focusing on methods like quantization, pruning, or batching strategies. Discuss your understanding of system-level performance and how you've successfully implemented these techniques to improve model efficiency.

Describe your strategies for keeping informed about advancements in audio ML, such as following key publications, participating in communities, and benchmarking state-of-the-art methods. This shows your commitment to continuous learning and professional development.

Talk about your experiences working collaboratively in a fast-paced environment. Highlight your ability to communicate effectively across different teams, contributing to project outcomes while adapting to changing priorities and workflows.

Discuss how low-level languages like C++ or Rust enhance performance in audio model development by providing greater control over system resources and optimizations. Share experiences where this expertise led to improved efficiency or capability in your projects.

Detail the key factors you evaluate when benchmarking methods, like latency, accuracy, and resource consumption. Providing insights into your systematic approach demonstrates your analytical skills and focus on delivering high-performance solutions.

Share your mentoring philosophy and discuss examples of how you've supported junior engineers or researchers in their development. Highlight specific techniques you use to foster knowledge sharing and skill development within your team.

Outline your strategies for deploying models efficiently, including considerations like scalability, model health checks, and rollbacks. Describe past deployment experiences where these strategies ensured smooth transitions into production.