Forward Deployed Engineer, Post-Training

Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own.

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.

What you'll do:

• 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.

Full details and application on the official Together AI careers page.