Forward Deployed Engineer, Post-Training
At a glance
Mid-level AI Research role at Together AI. San Francisco · $270,000–$300,000 OTE.
aimlroles summary, based on the employer's posting.
What you'll do
- Help customer teams improve post-training data, identify quality problems, and plan how to generate more training examples
- Choose suitable fine-tuning or reinforcement-learning methods, then design experiments and investigate instability or model regressions
- Build customer evaluations using task benchmarks, judge-model workflows and held-out data, tied to production outcomes
- Lead technical work for strategic accounts from initial scope through production, including workload sizing and delivery milestones
- Feed customer findings into product plans, contribute code and practical implementations, and support early feature adoption
What you bring
- At least five years in an ML engineering, applied AI, forward-deployed or solutions-architecture role, with recent hands-on LLM post-training
- Expert practical knowledge of LLM fine-tuning or reinforcement learning, including SFT, LoRA, DPO and methods such as GRPO or PPO
- A track record of rigorous experiments, ablation studies and evaluations, plus the ability to build and refine training datasets
- Strong Python skills, production coding experience, and sound judgment when choosing open-source base models for customer needs
- Experience leading complex customer-facing technical projects, with strong project management and communication skills
Who this fits
This role suits a hands-on post-training specialist who can guide customer teams while working closely with solutions architects, sales and capacity teams. You’ll lead strategic-account work through production and contribute as an individual technical lead. The stated US base salary range is $270,000 to $300,000, with OTE, equity and benefits; remote work flexibility is offered.
From the employer
About the role
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.
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. 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.
Requirements
- 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.
About Together AI
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.
Compensation
We offer competitive compensation, startup equity, health insurance, and other benefits, as well as flexibility in terms of remote work. The US base salary range for this full-time position is: $270,000 - $300,000 OTE + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.
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.
Please see our Privacy Policy at https://www.together.ai/privacy
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