Remote Forward Deployed Engineer – Post-Training AI

Together Computer Inc; San Francisco (CA); Remote; USD 270,000 - 300,000; Full time

Benefits offered by this job

Startup equity

Health insurance

Remote work flexibility

Job summary

Together AI, the AI Native Cloud, seeks a Forward Deployed Engineer focused on post-training to partner with our strategic customers and guide data strategy, algorithm selection, experimentation, and evaluation of post-training POCs.

You will size compute for RL and fine-tuning, drive platform adoption, and translate customer goals into measurable gains in model quality, contributing to customer success and company growth.

Qualifications

• 5+ years in a technical ML role with post-training LLM experience.
• Expert-level post-training with fine-tuning and RL (SFT, LoRA, DPO, PPO etc.).
• Strong experimental rigor in designing ablations and evaluations.

Responsibilities

• Data Strategy: Define and execute data strategy for post-training; diagnose data quality issues and recommend data generation.
• Algorithm Selection: Recommend the right post-training approach per use case.
• Experiment Design & Hyperparameter Tuning: Design controlled experiments and tracking methods; diagnose training instabilities.
• Evaluation: Build evaluation frameworks with benchmarks and held-out test sets to align with production metrics.
• Model Selection: Guide base model selection for post-training objectives.
• RL Workload Sizing: Size RL and fine-tuning workloads and shape capacity deals.
• Strategic Customer Alignment: Be primary post-training contact for strategic accounts; lead from scoping to production.
• Product & Research Feedback Loop: Contribute code and recipes; drive adoption of new features.

Skills

Python

Data strategy

Experiment design

RL platforms

Project management

Communication

Job description

Together AI, the AI Native Cloud, seeks a Forward Deployed Engineer focused on post-training to partner with our strategic customers and guide data strategy, algorithm selection, experimentation, and evaluation of post-training POCs.

You will size compute for RL and fine-tuning, drive platform adoption, and translate customer goals into measurable gains in model quality, contributing to customer success and company growth.