Member of Technical Staff - Model Training
Palo Alto, CA, United States
- Experience
- 0–30 years
- Employment
- Full-time
- Work mode
- Onsite
- Salary
- Not disclosed
- Deadline
- Apply by 1 Nov 2026
- Posted
- 2025-09-16
Required skills
| Skill | Experience | Level |
|---|---|---|
| Python | 3+ years | Advanced |
| Pytorch | 2+ years | Advanced |
| Model Training Using Reinforcement Learning | 2+ years | Intermediate |
| Multimodal Machine learning | 2+ years | Intermediate |
| Training Large-Scale LLMs | 2+ years | Intermediate |
About the role
Job Title:
Member of Technical Staff – Model Training
Role Overview
Join our Client, a fast-moving AI company focused on enterprise-grade conversational intelligence. This organization is a mission-driven public benefit corporation that equips businesses with customizable language models, proprietary data pipelines, and intelligent tuning systems—allowing virtual assistants to become smarter, more accurate, and brand-aligned over time.
This role sits at the intersection of ML research and production engineering. As a Model Training Engineer, you’ll help turn general-purpose LLMs into finely tuned, high-performing assistants using cutting-edge post-training and fine-tuning techniques. You'll have access to massive GPU clusters, real-world feedback loops, and the autonomy to experiment, iterate, and deploy improvements rapidly.
Key Responsibilities
- Develop and maintain scalable post-training workflows including dataset curation, evaluation, hyperparameter tuning, and rollout
- Experiment with and deploy advanced alignment methods such as RLHF, DPO, GRPO, and RLAIF
- Build training automation tools, dashboards, and pipeline components to improve reproducibility and traceability
- Define key training metrics, run A/B tests, and quickly iterate to hit performance goals
- Collaborate cross-functionally with inference, safety, and product teams to integrate model improvements into user-facing systems
Education & Qualifications
- Hands-on experience training large transformer models on distributed GPU systems (multi-GPU, multi-node)
- Strong proficiency with Python and PyTorch, including ecosystem tools like Torchtune, FSDP, and DeepSpeed
- Practical understanding of reinforcement learning techniques (RLHF, DPO, GRPO, RLAIF)
- Effective communicator across both technical and non-technical stakeholders
- Proven ability to build reproducible and automated training infrastructure
Preferred Experience
- Experience with multimodal (vision-language, audio-text) or voice models
- Familiarity with cross-modal data preparation and model alignment
- Contributions to open-source ML tooling
Why Our Client
- High-impact mission – Shape the future of enterprise AI by building assistants that reflect each brand’s voice authentically
- Massive compute resources – Access thousands of NVIDIA and Intel Gaudi GPUs for rapid iteration and experimentation
- Growth & autonomy – Competitive compensation ($200K–$350K base), meaningful equity, and ownership of critical projects
- Open-source culture – Actively contribute to projects like Torchtune, PyTorch, and vLLM; every engineer is encouraged to give back
Applicants must be currently authorized to work in the United States on a full-time basis now and in the future. This position does not offer sponsorship.