2 months ago
Responsibilities
- Translate ambiguous partner product specifications into concrete model-training requirements.
- Own the fine-tuning recipe for an on-device audio-to-function-calling model and maintain reliable tool calling across supported languages.
- Generate, clean, and analyze training data and build and maintain large-scale data pipelines.
- Run continuous model training and evaluation cycles against partner requirements through major software releases.
- Create dashboards, analyses, documentation, and polished partner-facing deliverables.
- Progressively own the end-to-end model development pipeline from requirements intake through delivered checkpoint.
- Participate in partner calls and communicate technical work clearly to audiences without technical context.
Requirements
- Approximately 2+ years of hands-on machine-learning experience, with exceptional early-career candidates considered if they have strong internship experience.
- Personally trained models end-to-end; application development using model APIs alone does not qualify.
- Experience building or working with large-scale data pipelines and wrangling large volumes of data.
- Experience in an automotive, embedded-device, or on-device machine-learning context.
- Strong communication skills for daily interaction with clients and internal teams.
- Audio or speech model experience is preferred.
- Function-calling or tool-use fine-tuning experience is preferred.
- Multilingual model or data experience is preferred.
- Experience at an automotive, autonomous-vehicle, or defense research laboratory is preferred.
Benefits
- 100% employer-paid medical, dental, and vision premiums for employees and dependents.
- 401(k) matching up to 4% of base pay.
- Unlimited paid time off and company-wide Refill Days.
- End-to-end ownership of a production model pipeline with direct mentorship.
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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.