Forward Deployed Engineer, Post-Training

About the role

Together AI is hiring a Forward Deployed Engineer (FDE) focused on Post-Training, based in San Francisco. As a hands-on technical partner to its strategic customers, you will support AI teams that use fine-tuning and reinforcement learning to build differentiated models on open-source foundations. You work alongside Solutions Architects 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. The role does not replace Solutions Architects.

Key responsibilities

• Data strategy: help customers define and execute their data strategy for post-training, diagnose data quality issues and recommend data generation approaches
• Algorithm selection: recommend the right post-training approach for each use case
• Experiment design and hyperparameter tuning: design controlled experiments and tracking methods, and 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
• Model selection: guide base model selection and baseline quality evaluations before post-training, and show interest in researching newly released open-source models
• RL workload sizing: scope and size RL and fine-tuning workloads and partner with sales and capacity teams on deals and forecasts
• Strategic customer alignment: act as the primary technical point of contact for aligned strategic accounts, leading execution from scoping to production
• Product feedback loop: shape the fine-tuning and RL product roadmap with field insights, contribute code, recipes and reference implementations, and drive early adoption of new features

Required profile

• 5+ years in a technical role in ML engineering, Applied AI, Forward Deployed Engineering or Solutions Architect, with recent hands-on post-training LLM experience
• 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
• Broad knowledge of state-of-the-art open-source models and sound judgment on which base model to post-train
• Proven ability to build and iterate on training datasets
• Experience leading complex, customer-facing technical projects, with strong project management and communication skills in a highly cross-functional environment

Required skills

• Strong Python skills
• Comfortable working in production environments

What we offer

• US base salary range of $270,000 - $300,000 OTE plus equity and benefits
• Health insurance and other benefits
• Flexibility in terms of remote work