Senior Machine Learning Engineer - Generative Models (Remote)

We are a producers playground, delivering music creators the tools they need to bring their ideas to life. With a massive, industry-leading catalog of licensed samples, paired with powerful AI, and access to affordable plugins and DAWs, Splice kicks sound discovery, inspiration, and creative output into overdrive.

At Splice, DISCO is a rallying cry for collaboration, accountability, and unity within our organization; Direct, Inclusive, Splice Together, Creator Centric, and Optimistic. Our shared success depends on our ability to support one another, work well together, and communicate directly. By embracing flexibility and a unified approach, we can navigate anything that's thrown at us. Splice embraces a culture of remote work. You'll see your colleagues showing up from across the US and the UK. In order to keep us working well as a team, we have regular communication, including Town Halls, departmental All Hands, and get-togethers. When you join Splice, you join a network of colleagues, peers, and collaborators. Are you ready?

JOB TITLE: Senior Machine Learning Engineer
LOCATION: Remote / NY
TEAM INFORMATION: The Splice AI & Audio Science team is dedicated to pushing the boundaries of artificial intelligence applied to audio data, with the mission to empower music creators everywhere. Being musicians ourselves, we are deeply committed to the use of AI in a creator-centric, ethical, and responsible way. Our team consists of passionate and creative individuals who thrive in a collaborative, innovative, and fast-paced environment.

WHAT YOU WILL DO:
- Design, adapt, and optimize cutting-edge model architectures for generative audio/music applications, leveraging state-of-the-art deep learning techniques for audio/music synthesis.
- Collaborate with Applied Researchers and Machine Learning Engineers to design, train, fine-tune, and deploy scalable models to production.
- Explore and implement core building blocks in generative models, such as VAEs, Neural Audio Codecs, GANs, Diffusion Models, and Transformer-based architectures.
- Integrate machine learning models into Splice's products, delivering new and creative experiences for music creators.
- Design and run experiments for performance benchmarking and evaluation of trained models.
- Stay current with advancements in machine learning for generative models in audio, sharing insights in the applied research process.
- Document experiments, best practices, and lessons learned, while providing technical guidance and training on model training, evaluation, deployment, and optimization techniques.

JOB REQUIREMENTS:
- Master's or PhD degree in Electrical Engineering, Computer Science, or related Engineering discipline.
- Proven ability to design, train, evaluate, and deploy machine learning models in production environments.
- 2+ years of hands-on experience with generative model architectures in audio, image, or language domains, with specific experience in Latent Diffusion Models and Transformer-based architectures.
- Proficiency in Python, C/C++, or CUDA, and strong expertise in TensorFlow or PyTorch.
- Experience with cloud services like AWS, Azure, GCP, and containerization technologies such as Docker and Kubernetes.
- Familiarity with software development best practices and version control systems like Git.

NICE TO HAVES:
- Familiarity with audio signal processing, music information retrieval (MIR), or audio synthesis techniques.
- Background or knowledge in music production.

The national pay range for this role is $165,000 - $206,000. Individual compensation will be commensurate with the candidate's experience.

Splice is an Equal Opportunity Employer. We provide equal employment opportunities to all employees and applicants for employment and prohibit discrimination and harassment of any type based on race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.