Post-Training ML Engineer — RL & Fine-Tuning Specialist

Fdetimes; San Francisco (CA); Remote; USD 270,000 - 300,000; Full time

Benefits offered by this job

Startup equity

Health insurance

Flexible remote work

Job summary

Together AI, the AI Native Cloud, seeks a Forward Deployed Engineer focused on post-training to partner with our strategic customers. You will guide data strategy, algorithm selection, experiment design, and compute sizing behind post-training POCs, driving adoption of fine-tuning and RL on open-source foundations.

You will lead technical execution from scoping to production for aligned strategic accounts, shape product roadmap with field insights, and contribute code, recipes, and reference

Qualifications

• 5+ years in ML engineering, Applied AI, FDE, or Solutions Architect roles
• Hands-on post-training LLM experience with fine-tuning and RL techniques (SFT, LoRA, DPO, PPO)
• Strong experimental design and evaluation experience
• Ability to build and iterate on training datasets
• Broad knowledge of open-source models and model selection for post-training

Responsibilities

• Define and guide data strategy for model post-training and diagnose data quality issues
• Recommend post-training approaches for each use case
• Design controlled experiments and tracking to diagnose training issues
• Build evaluation frameworks with benchmarks and held-out test sets
• Guide base model selection for post-training and explore open-source models
• Size RL and fine-tuning workloads and collaborate with sales for capacity forecasts
• Serve as primary contact for aligned strategic accounts and drive production execution
• Contribute to product roadmap with field insights and reference implementations

Skills

Python

Post-training LLMs

Fine-tuning & RL

Experiment design

Data strategy

Communication

Production experience

Tools

LoRA

SFT

RL frameworks

Job description

Together AI, the AI Native Cloud, seeks a Forward Deployed Engineer focused on post-training to partner with our strategic customers. You will guide data strategy, algorithm selection, experiment design, and compute sizing behind post-training POCs, driving adoption of fine-tuning and RL on open-source foundations.

You will lead technical execution from scoping to production for aligned strategic accounts, shape product roadmap with field insights, and contribute code, recipes, and reference