[Hiring] Forward Deployed Engineer, Post-Training @Together AI

Role Description

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
• 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. Ensure 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 ensure critical milestones are met.
• 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.

Qualifications

• 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.
• Expert-level, hands-on experience with fine-tuning and/or RL for LLMs, including SFT, LoRA, DPO, and RL (GRPO, PPO, etc.).
• Strong track record designing experiments, running ablations, and building evaluations that separate real improvement from noise.
• Proven ability to build and iterate on training datasets.
• Broad knowledge of state-of-the-art open-source models and strong judgment on which base model to post-train for given customer requirements.
• 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 cross-functional environment.

Benefits

• Competitive compensation
• Startup equity
• Health insurance
• Flexibility in terms of remote work

Company Description

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.