# Research Scientist, Post-Training

**Company:** [Datology AI](https://hotfix.jobs/companies/datology-ai)
**Location:** Redwood City, CA
**Role:** AI Research
**Salary:** $180k – $300k/yr
**Experience:** 3+ years
**Skills:** PyTorch, Dpo, Rlvr, Rrhf, Sft, Instruction Tuning, Preference Tuning, Synthetic Data, Data Curation, Multimodal Models
**Posted:** 2025-07-16

> Leads research on post-training data curation for foundation models, designing algorithms to generate/improve instruction and preference datasets, and unifying pre/post-training optimization. Requires 3+ years deep learning research, post-training experience with vision/language/multimodal models, and PyTorch proficiency.

## Job Description

## What You'll Work On

- Post-training data curation: conduct research on algorithmically curating post-training data (e.g., generating/refining preference and instruction-following data, curating capability/domain-specific data, making post-training more effective/controllable/generalizable).
- Unifying pre-training and post-training data curation: pursue research on end-to-end data curation (curate pre-training data to improve post-trainability, jointly optimize pre/post-training data to maximize final model performance).
- Transform messy literature into practical improvements: source, vet, implement, and improve promising ideas from literature or your own creation.
- Conduct science driven by real-world needs: guided by customer needs and product improvements.

## About You

**Required:**
- 3+ years of deep learning research experience
- Experience with post-training large vision, language, and multimodal models
- Post-training algorithm development, data curation, and/or synthetic data methods for:
  - Preference-based tuning (e.g. **DPO**, **RLVR**, **RRHF**)
  - Alternative supervision & self-supervision techniques (e.g. self-training, chain-of-thought distillation)
  - **SFT** (e.g. instruction tuning, demonstration fine-tuning)
- Post-training tooling development and engineering experience
- Strong understanding of deep learning fundamentals
- Software engineering + deep learning framework (**PyTorch** or willingness to learn) skills for large-scale experiments and production prototypes
- Track record of success in deep learning research (papers, tools, artifacts)

**Nice-to-haves:**
- Experience with data management and distributed data processing (**Spark**, **Snowflake**, etc.)
- Experience building + shipping ML products

## Compensation

- Base salary: **$180,000 - $300,000**
- Significant equity
- 100% covered health benefits (medical, vision, dental)
- 401(k) with 4% company match
- Unlimited PTO
- Annual $2,000 wellness stipend
- Annual $1,000 learning stipend
- Daily lunches/snacks
- Relocation assistance to Bay Area

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