7 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
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