8 months ago
Responsibilities
- Own enterprise customer post-training engagements for text workloads from requirements through delivery and evaluation.
- Translate customer requirements into concrete post-training specifications and workflows.
- Design and execute data generation, filtering, and quality-assessment processes for text corpora.
- Run supervised fine-tuning, instruction tuning, RLHF, DPO, and other preference-alignment workflows.
- Design task-specific evaluations for text-model performance and interpret results.
- Build reusable applied tooling and workflows that accelerate future customer engagements.
- Serve as the technical bridge between customer needs and internal technical teams.
Requirements
- Hands-on experience with data generation and evaluation for LLM post-training.
- Experience training or fine-tuning models using supervised fine-tuning, instruction tuning, RLHF, DPO, or similar preference-alignment methods.
- Strong intuition for text data quality and evaluation design.
- Experience with text-specific post-training workflows, such as chat model alignment, instruction tuning, or large-scale text data curation.
- Proficiency with the open-source ML ecosystem, including Hugging Face and PyTorch, and modern model architectures.
- Experience delivering applied ML work to external customers with measurable outcomes is preferred.
- Familiarity with vLLM, SGLang, or TensorRT is preferred.
- Experience building reusable ML tooling or evaluation infrastructure is preferred.
Benefits
- Competitive base salary with equity in a unicorn-stage company; compensation amount is not specified.
- Medical, dental, and vision premiums fully covered for employees and dependents.
- 401(k) matching up to 4% of base pay.
- Unlimited paid time off and company-wide Refill Days throughout the year.
Tech Stack
Categories
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.