Forward Deployed Engineer, Post-Training

Together AI•San Francisco, CA

1d•$270,000 - $300,000

About The Position

As a Forward Deployed Engineer focused on Post-Training, you will serve as a hands-on technical partner to strategic customers using fine-tuning and reinforcement learning to build differentiated models on open-source foundations. You will work alongside Solutions Architects as the post-training specialist, guiding customers through data strategy, algorithm selection, experiment design, evaluation, and compute sizing. The role supports post-training proofs of concept, adoption of the fine-tuning and RL platform, and measurable improvements in model quality. The work also contributes to customer success, company growth, and the development of the core platform. Together AI describes itself as an AI Native Cloud built for AI engineers, offering inference, fine-tuning, reinforcement learning, pre-training, and a marketplace of open models. The company serves more than 400 trillion tokens per month and names Cursor, Decagon, ElevenLabs, Salesforce, and Zoom among its customers. This is a full-time position with a US base salary range of $270,000–$300,000 OTE, plus equity and benefits. Compensation depends on location, level, experience, skills, and job-related knowledge. The company offers flexibility in terms of remote work.

Requirements

At least 5 years in a technical role in ML engineering, Applied AI, Forward Deployed Engineering, or Solutions Architecture, with recent hands-on experience post-training LLMs.
Expert-level hands-on experience with LLM fine-tuning and/or reinforcement learning, including SFT, LoRA, DPO, and RL methods such as GRPO and PPO.
A strong track record designing experiments, running ablations, and building evaluations that distinguish real improvements from noise.
Proven ability to build and iterate on training datasets.
Broad knowledge of state-of-the-art open-source models and sound judgment in selecting a base model to post-train for customer requirements.
Strong Python skills and comfort working in production environments.
Experience leading complex, customer-facing technical projects, with exceptional project-management and communication skills and the ability to work in a highly cross-functional environment.

Responsibilities

Help customers define and execute data strategies for model post-training, diagnose data-quality issues that limit performance, and recommend data generation for post-training at scale.
Recommend suitable post-training approaches for individual use cases.
Design 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, and ensure improvements reproducibly align with production success metrics.
Guide base-model selection and baseline quality evaluations before post-training, including self-directed research on newly released open-source models.
Scope and size RL and fine-tuning workloads, and partner with sales and capacity teams on deals and capacity forecasts.
Serve as the primary post-training technical point of contact for aligned strategic accounts, leading technical execution from scoping through production and ensuring critical milestones are met.
Shape the fine-tuning and RL product roadmap using field insights; contribute code, recipes, and reference implementations as needed; and drive early adoption of new features and research with strategic customers.