Forward Deployed Engineer, Post-Training

<h3><strong>About the role</strong></h3> <p>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.</p> <h3><strong>Responsibilities</strong></h3> <ul> <li><strong>Data Strategy:</strong> Help customers define and execute their data strategy for model post-training,&nbsp; diagnose data quality issues that limit model performance outcomes, and provide recommendations for data generation for post-training scale</li> <li><strong>Algorithm Selection:</strong> Recommend the right post-training approach for each use case</li> <li><strong>Experiment Design &amp; Hyperparameter Tuning:</strong> Design well-controlled experiments and tracking methods; diagnose training instabilities, reward hacking, and regressions.</li> <li><strong>Evaluation:</strong> 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.</li> <li><strong>Model Selection:</strong> 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.</li> <li><strong>RL