Forward Deployed Engineer, Post-Training

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.

• →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.

• 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 and communication skills. Able to work in a highly X-functional environment.

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. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month.

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