10 months ago
Responsibilities
- Build and scale audio-model training data pipelines, including preprocessing, augmentation, and quality filtering
- Design, implement, and maintain evaluation systems for multimodal performance across internal and public benchmarks
- Fine-tune and adapt audio models for customer-specific use cases from requirements through deployment
- Contribute production code to the core audio repository while collaborating with infrastructure and research teams
- Support experimentation under real hardware constraints and shift between customer work and core development as priorities change
- Own customer workstreams and deliver production-ready pipelines or evaluation systems
Requirements
- Strong programming fundamentals and the ability to write clean, maintainable, production-grade code
- Experience building and shipping production ML systems beyond model training, including data pipelines, evaluation systems, or serving infrastructure
- Proficiency in PyTorch and familiarity with distributed training frameworks such as DeepSpeed or FSDP
- Experience collaborating in shared codebases with high engineering standards
- Direct experience with audio or speech models such as ASR, TTS, vocoders, diarization, or speech-to-speech systems is preferred
- Experience running large-scale training experiments on distributed GPU clusters is preferred
- Open-source contributions demonstrating code quality and engineering judgment are preferred
Benefits
- 100% of medical, dental, and vision premiums covered for employees and dependents
- 401(k) matching up to 4% of base pay
- Unlimited PTO and company-wide Refill Days
- Equity offered
- Work involves production systems spanning data-center accelerators and on-device hardware
Tech Stack
Categories
Data EngineeringML Engineering
About Liquid AI
Liquid AI builds general-purpose AI systems that run efficiently from data center accelerators to on-device hardware, emphasizing low latency, memory efficiency, privacy, and reliability. The company partners with enterprises in consumer electronics, automotive, life sciences, and financial services to deploy and benchmark models for real-world workloads. Founded in 2023 out of MIT CSAIL and headquartered in Cambridge, Massachusetts, it is privately held.