# Forward Deployed Engineer, Post-Training at Together AI

> Guide strategic customers through fine-tuning and RL, from data strategy and experiments to evaluation and workload sizing.

- Company: Together AI (together.ai)
- Location: San Francisco
- Places: San Francisco, United States
- Posted: 2026-10-24
- Apply: https://job-boards.greenhouse.io/togetherai/jobs/5274195007
- Page: https://ye.saranatour.dev/jobs/forward-deployed-engineer-post-training-together-ai-kn7f6maffn56mss5ft4ceenpa58hdc12

## The posting

Forward Deployed Engineer, Post-Training
Together AI · San Francisco

### **About the role**

As a Forward Deployed Engineer (FDE) focused on Post-Training, you will be a hands-on technical partner to our most strategic customers: AI teams using fine-tuning and reinforcement learning to build differentiated models on open-source foundations. FDE does not replace our Solutions Architects. You will work alongside them as the deep-domain specialist in post-training, guiding customers through data strategy, algorithm selection, experiment design, and evaluation, and sizing the compute behind it. You’ll make sure we can deliver on our post-training POCs, drive adoption of our fine-tuning and RL platform, and turn customer goals into measurable gains in model quality. Your work directly affects customer success, company growth, and the hardening of our core platform.

### **Responsibilities**

- **Data Strategy:** Help customers define and execute their data strategy for model post-training, diagnose data quality issues that limit model performance outcomes, and provide recommendations for data generation for post-training scale
- **Algorithm Selection:** Recommend the right post-training approach for each use case
- **Experiment Design & Hyperparameter Tuning:** Design well-controlled experiments and tracking methods; diagnose training instabilities, reward hacking, and regressions.
- **Evaluation:** Build evaluation frameworks with customers, including task-specific benchmarks, LLM-as-judge pipelines, and held-out test sets. Make sure gains are reproducibly aligned with production success metrics.
- **Model Selection:** Guide base model selection methodology ahead of post-training efforts and baseline quality evaluations to guide post-training objectives. Interest in self-directed research of newly released open source models, particularly as they relate to post-training.
- **RL Workload Sizing:** Scope and size RL and fine-tuning workloads, partner with sales and capacity teams to shape deals and capacity forecasts.
- **Strategic Customer Alignment:** Serve as the primary post-training technical point of contact for aligned strategic accounts. Lead technical execution from scoping to production and make sure we hit critical milestones.
- **Product & Research Feedback Loop:** Shape our fine-tuning and RL product roadmap with insights from the field. Contribute code, recipes, and reference implementations where needed, and drive early adoption of new features and research with strategic logos.

### Requirements

- **Experience:** 5+ years in a technical role in ML engineering, Applied AI, Forward Deployed Engineering, or Solutions Architect, with recent hands-on experience post-training LLMs.
- **Post-Training Depth:** Expert-level, hands-on experience with fine-tuning and/or RL for LLMs, including SFT, LoRA, DPO, and RL (GRPO, PPO, etc)..
- **Experimental Rigor:** Strong track record designing experiments, running ablations, and building evaluations that separate real improvement from noise.
- **Data Intuition:** Proven ability to build and iterate on training datasets
- **Model Landscape Awareness:** Broad knowledge of state-of-the-art open-source models and strong judgment on which base model to post-train for a given customer requirements
- **Coding Proficiency:** Strong Python skills; comfortable working in production environments.
- **Experience leading complex, customer-facing technical projects**, with exceptional project management