Member of Technical Staff Multi Modal Audio
Liquid AI is an efficiency-first foundation-model company building device-native Liquid Foundation Models (LFMs) and tools to customize and deploy them.
Funding history
About Liquid AI
An MIT CSAIL spinout, Liquid AI develops general-purpose AI models focused on efficient deployment across CPUs, GPUs, NPUs, edge devices, and cloud or on-premises environments.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will build scalable audio-training data pipelines, including preprocessing, augmentation, and quality filtering. You will create and maintain multimodal evaluation systems, fine-tune audio models for customer use cases, contribute production code, and support experimentation under real hardware constraints.
Requirements
- Strong programming fundamentals
- Ability to write clean, maintainable, production-grade code
- Experience building and shipping production ML systems beyond model training
- Proficiency in PyTorch
- Familiarity with distributed training frameworks such as DeepSpeed or FSDP
- Experience collaborating in shared codebases with high engineering standards
Responsibilities
- Build and scale data pipelines for audio model training
- Design, implement, and maintain multimodal evaluation systems
- Fine-tune and adapt audio models for customer-specific use cases
- Own customer delivery from requirements through deployment
- Contribute production code to the core audio repository
- Support experimentation under real hardware constraints
Benefits
- Equity
- Medical, dental, and vision premiums fully paid for employees and dependents
- 401(k) matching up to 4% of base pay
- Unlimited PTO
- Company-wide Refill Days