Forward Deployed Engineer, Post-Training

Posted 14 Hours Ago
Be an Early Applicant
San Francisco, CA, USA
In-Office
270K-300K Annually
Senior level
Artificial Intelligence • Information Technology
The Role
Lead customer-facing post-training initiatives for large language models, including data strategy, algorithm selection, experiment design, evaluation, model selection, and RL workload sizing. Guide strategic customers from scoping through production, deliver measurable model-quality improvements, contribute code and reference implementations, and provide feedback to product and research teams. Requires deep hands-on expertise with LLM fine-tuning, reinforcement learning, experimentation, evaluation, datasets, open-source models, and production Python engineering.
Summary Generated by Built In
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

Skills Required

  • 5+ years of experience in ML engineering, Applied AI, Forward Deployed Engineering, Solutions Architecture, or a related technical role
  • Recent hands-on experience post-training large language models
  • Expert-level hands-on experience with LLM fine-tuning and/or reinforcement learning, including SFT, LoRA, DPO, GRPO, and PPO
  • Strong experience designing experiments, running ablations, and building evaluations that distinguish real improvement from noise
  • Proven ability to build and iterate on training datasets
  • Broad knowledge of state-of-the-art open-source models and ability to select appropriate base models
  • Strong Python skills and comfort working in production environments
  • Experience leading complex, customer-facing technical projects
  • Exceptional project management and communication skills
  • Ability to work effectively in a highly cross-functional environment

Together AI Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Together AI and has not been reviewed or approved by Together AI.

  • Fair & Transparent Compensation — Pay is considered competitive for the company’s stage, with especially strong offers for technical and research roles. A posted AI researcher range reaching into the high-$200Ks alongside equity and benefits reinforces the overall pay positioning.
  • Equity Value & Accessibility — Equity is commonly included alongside base salary, giving employees ownership exposure tied to company growth. Total compensation is often framed around salary plus equity for specialized roles.
  • Flexible Benefits — Job listings describe a flexible time‑off policy and hybrid or remote work options. Commuter perks and regular team events are also cited as part of the package.

Together AI Insights

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The Company
HQ: Surry Hills
84 Employees
Year Founded: 2022

What We Do

Together AI is a research-driven artificial intelligence company. We contribute leading open-source research, models, and datasets to advance the frontier of AI. Our decentralized cloud services empower developers and researchers at organizations of all sizes to train, fine-tune, and deploy generative AI models. We believe open and transparent AI systems will drive innovation and create the best outcomes for society

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