Machine Learning Engineer - Model Optimization (Remote)

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: Machine Learning Engineer

LOCATION: Remote 

THE ROLE:

We are seeking a highly skilled and experienced Machine Learning Engineer with a strong focus on model design and optimization, and a proven track record of deploying machine learning models in production environments, serving real applications. As a key member of our AI team, you will play a pivotal role in improving the performance, cost efficiency, and scalability of our machine-learning models delivering generative audio capabilities.

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:

• Model optimization: work closely with our Applied Researchers, Machine Learning Engineers and platform engineers to propose, design and implement model optimization strategies, tailored to specific use cases, resource constraints and deployment scenarios.
• Model Analysis and Profiling: conduct thorough analysis and profiling of machine learning models to identify computational bottlenecks and resource-intensive operations.
• Model quantization and compression: implement quantization and compression techniques to reduce the memory footprint and computational requirements of machine learning models with minimum impact on accuracy. Experiment with different quantization methods to achieve optimal trade-offs between model size, inference latency, cost and accuracy.
• Performance Benchmarking and Evaluation: design rigorous experiments to benchmark the performance of optimized models across various optimization settings and inference scenarios. Evaluate the impact of optimization techniques on inference, memory usage, power consumption, and other relevant metrics.
• Documentation and Knowledge Sharing: document optimization procedures, best practices, and lessons learned to facilitate knowledge sharing and maintain reproducibility. Provide technical guidance and training to team members on model optimization techniques and tools.

JOB REQUIREMENTS:

• Master's degree in Electrical Engineering, Computer Science or related Engineering discipline.
• Previous experience designing, training and deploying machine learning models in production environments, powering real applications.
• Proficiency in programming languages such as Python, C/C++, or CUDA. Strong proficiency in machine learning frameworks (e.g., TensorFlow, PyTorch).
• Experience with model optimization techniques, including quantization, pruning, and distillation.
• Hands-on experience with cloud services (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes).
• Familiarity with software development best practices and version control systems (e.g., Git).
• Experience with continuous integration/continuous deployment (CI/CD) pipelines is a plus.

NICE TO HAVES:

• Background or experience in Digital Signal Processing.
• Experience with Diffusion-based generative models.
• 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.