Sr. Machine Learning Engineer

Positive Grid is devoted to creating cutting-edge technologies to revolutionize the world's music production. Machine learning team is looking for a proactive, good-communicator and hardworking ML engineer to advance the technologies of guitar/music production. Our projects involve music information retrieval, audio effects chain generation, guitar amp and analog effect modeling, multi-modal LLM, and other machine learning based technologies to extend the Positive Grid's guitar music creation ecosystem.

You are expected to not only perform research and development but also provide thorough evaluation and proposal documents for any major technical decisions. This includes explaining why you choose a certain framework or approach (e.g., model architecture, data manipulation, feature extraction, etc.) by assessing aspects such as binary size, cost, features, maintainability, and integration effort. You will articulate these decisions clearly to stakeholders, proactively address inquiries, and demonstrate an open-minded approach to trying new toolsespecially AI-based toolswhile providing concrete justifications if you decide not to adopt them.

Typical Accountabilities

1. Research and apply advanced multi-media generative models, such as those used in audio and image generation, along with Virtual Analog (VA) Modeling (e.g., Amp/Pedal Modeling), Music Information Retrieval, and other machine learning topics relevant to guitar and audio.

2. Collaborate with DSP engineers and the product team to create innovative technology solutions for music-related challenges.

3. Design and implement real-time, neural network-based audio applications.

4. Work in partnership with application engineers to incorporate machine learning libraries into various applications.

5. Provide clear and well-structured technical presentations or documents (e.g., proof-of-concept reports, roadmap evaluations) to explain your reasoning for technology adoption or rejection.

6. Demonstrate a willingness to explore and assess AI-based tools (e.g., Deep Research, N8N) to improve development efficiency and research processes, and share findings with the team and leadership.

Essential

- Possess a Master's degree in Computer Science, Electrical Engineering, Mathematics, Physics, or a related field, or equivalent industry experience.

- Have a robust theoretical and practical understanding of deep learning and machine learning.

- Bring at least two years of experience in designing and optimizing deep neural network models, with a preference for candidates who have a relevant thesis or publication in machine learning.

- Demonstrating experience in developing and optimizing deep learning models for audio or computer vision applications.

Development Tool

- Hands-on experience of deep learning frameworks such as PyTorch, Tensorflow, etc.

- Experience with training ML projects with large-scale datasets.

- Experience with building ML project prototypes with Gradio, Streamlit, or standalone apps.

- Openness to adopting AI-related tools or platforms (e.g., Deep Research, GitHub Copilot, N8N) to enhance efficiency, while providing data-driven justification for your choices.

Desirable

- Electric guitar playing experience is a great plus.

- Having a foundational knowledge of Digital Signal Processing (DSP) theory.

- Experience with implementing ML models on embedded devices.

- Experience with audio plug-in frameworks like JUCE or iPlug2.