Together AI FDE
Forward Deployed Engineer, Post-Training
- Location San Francisco
- Seniority senior
- Posted 2026-10-24
Original posting ↗ You apply on the company site — we never collect applications.
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role details
Works with AI teams on post-training tasks such as data strategy, algorithm selection, and experiment design.
Summary generated by AI from the original posting.
Hard requirements to check first
- Clearance:not mentioned in the posting
- Work auth:not mentioned in the posting
- Travel:customer-facing; travel not stated
Skills
PythonFineTuningEvalsML engineeringApplied AIForward Deployed EngineeringSolutions Architectfine-tuningreinforcement learningopen-source foundationsdata strategy
Excerpt from the original posting
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 ro…
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