WHO WE ARE:

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.

HOW WE WORK:

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 other 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 general Variational Autoencoders (VAEs), Neural Audio Codecs (RVQ / VAE), GANs, Diffusion Models, and Transformer-based architectures.
Contribute to integrating machine learning models into Splice's products, delivering new and creative experiences for music creators.
Performance Benchmarking and Evaluation**:** design and run experiments to benchmark the accuracy, quality and performance of trained models.
Stay current with the latest advancements in machine learning applied to generative models in the audio domain, incorporating and sharing relevant insights into the applied research process.
Documentation and Knowledge Sharing**:** document experiments, best practices, and lessons learned to facilitate knowledge sharing and maintain reproducibility. Provide technical guidance and training to team members 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 and track record designing, training, evaluating and deploying machine learning models in production environments, powering real applications.
2+ years of hands-on experience with generative models architectures in the audio, image or language domains. Specific experience with Latent Diffusion Models and Transformer-based architectures is a must.
Proficiency in Python, C/C++, or CUDA. Strong proficiency in machine learning frameworks (e.g., TensorFlow, PyTorch).
Hands-on experience with cloud services (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes).
Comfortable with software development best practices and version control systems (e.g., Git).

NICE TO HAVES:

Familiarity with audio signal processing, music information retrieval (MIR), or audio synthesis techniques is a strong plus.
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

Splice provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to 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.