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About the Role
As a Forward Deployed Engineer (FDE) focused on Post-Training, you will be a hands-on technical partner to strategic customers building differentiated models on open-source foundations using fine-tuning and reinforcement learning. You will guide customers through data strategy, algorithm selection, experiment design, evaluation, and compute sizing, ensuring successful post-training POCs and driving adoption of our platform to achieve measurable gains in model quality. Your work directly impacts customer success, company growth, and platform development.
Responsibilities
- Help customers define and execute their data strategy for model post-training, diagnose data quality issues, and provide recommendations for data generation.
- Recommend the right post-training approach for each use case.
- Design well-controlled experiments and tracking methods; diagnose training instabilities, reward hacking, and regressions.
- Build evaluation frameworks with customers, including task-specific benchmarks, LLM-as-judge pipelines, and held-out test sets, ensuring gains align with production success metrics.
- Guide base model selection methodology and baseline quality evaluations to inform post-training objectives. Conduct self-directed research of new open-source models relevant to post-training.
- Scope and size RL and fine-tuning workloads, partnering with sales and capacity teams for deal shaping and capacity forecasts.
- Serve as the primary post-training technical point of contact for strategic accounts, leading technical execution from scoping to production and ensuring milestone achievement.
- Shape the fine-tuning and RL product roadmap with field insights. Contribute code, recipes, and reference implementations, and drive early adoption of new features and research with strategic customers.
Requirements
- 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.
- Ability to work in a highly X-functional environment.
Skills
- Fine-tuning
- Reinforcement Learning (RL)
- SFT
- LoRA
- DPO
- GRPO
- PPO
- Python
- Project Management
- Communication
Work Type
- Full-time
- Remote
Experience Level
- 5+ years
Salary/Compensations
- $270,000 - $300,000 OTE
Benefits
- Startup equity
- Health insurance
- Other benefits
About the Company
- 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.
Equal Opportunity
- Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.