---
title: 'Forward Deployed Engineer, Post-Training at Together AI'
canonical: 'https://feeny.ai/job/forward-deployed-engineer-post-training-together-ai-san-francisco-57ckxj1h1nmb'
type: 'job'
last_seen: '2026-11-06'
---

# Forward Deployed Engineer, Post-Training at Together AI

- **Company:** Together AI
- **Location:** San Francisco, CA
- **Posted:** 2026-10-24
- **Last confirmed live:** 2026-11-06
- **Apply:** https://job-boards.greenhouse.io/togetherai/jobs/5274195007

## Job description

## 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.

Application Policy

Please read each role carefully and apply to the position best suited to you. Candidates are limited to 6 applications within a 90-day period, and applications beyond that limit will be automatically rejected. You may apply to additional roles 90 days after your last application.

## 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

## About Together AI

## Company Overview
- **One-liner**: Together AI is the AI Native Cloud, a full-stack platform for production AI that provides inference, fine-tuning, and GPU compute powered by cutting-edge systems research.
- **Entity Type**: Private (Series C)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2022
- **Founders**: Vipul Ved Prakash (CEO), Ce Zhang (CTO), Tri Dao (Chief Scientist), Chris Ré, Percy Liang

## Core Business
- **Primary industry**: AI Infrastructure / Cloud Computing
- **Target customers**: B2B – AI-native startups (e.g., Cursor, Decagon, Eleven Labs), enterprise SaaS (Salesforce, Zoom, Zomato), and AI researchers.
- **Mission**: “Significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models” — with a commitment to open and transparent AI development.

## Products & Services
- **Serverless Inference**: Fast on-demand inference for open-source models with no infrastructure management.
- **Batch Inference**: Asynchronous processing of massive workloads (up to 30B tokens per model) at reduced cost.
- **Dedicated Model Inference**: Deploy models on dedicated GPU infrastructure for speed and control.
- **Dedicated Container Inference**: GPU infrastructure optimized for generative media (video, audio, image).
- **Fine-Tuning**: Fine-tune open-source models using latest research techniques (improves accuracy, reduces hallucinations).
- **Accelerated Compute**: Self-serve instant clusters to thousands of GPUs, optimized with Together Kernel Collection.
- **Model Shaping**: Tools to accelerate inference, model shaping, and pre-training.
- **Managed Storage**: High-performance object storage and parallel filesystems with zero egress fees.
- **Sandbox**: Fast, secure code sandboxes for AI app/agent development environments.

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed; recent Series C reportedly doubled valuation (amount undisclosed) [cbinsights.com](https://www.cbinsights.com/company/together-4)
- **Key Metric**: **Total Funding** – $1.334B (including $800M Series C announced in mid-2026) [cbinsights.com](https://www.cbinsights.com/company/together-4)
- **Notable Investors/Partners**: Kleiner Perkins, NVIDIA, Salesforce Ventures, Prosperity7 Ventures (Aramco), and 29+ others across rounds.
- **Growth Signals**: 
  - Headcount grew 91% YoY to ~280 employees [linkedin.com](https://www.linkedin.com/company/togethercomputer)
  - Acquired CodeSandbox (Dec 2024) and Refuel.AI (May 2025)
  - Customers include Cursor, Decagon, Eleven Labs, AI21, Hedra, Cartesia, Salesforce, Zoom, Zomato
  - Published nine papers at ICML 2026; open-sourced ParallelKernelBench

## Competitive Advantages
- **Full-stack AI platform** covering inference, fine-tuning, compute, storage, and sandboxes — all integrated.
- **World-class research team** co-designing algorithms, kernels, and hardware (e.g., Tri Dao, co-inventor of FlashAttention).
- **Open-source commitment** and contributions (models, datasets, research) build developer trust and ecosystem lock-in.
- **Cost performance**: Claims 2x faster inference and 60% lower cost vs. alternatives through research-driven optimizations.

## Strategic Focus
- Accelerate the shift to open-source AI by making it economically viable at scale.
- Continue investing in systems research (GPU programming, kernel optimization, model shaping) to improve performance and cost.
- Expand enterprise adoption through dedicated deployments and partnerships.
- Grow the platform’s capabilities (e.g., managed storage, sandboxes) to reduce friction for AI-native builders.

## Why Work Here
- **Culture**: Values include “Do more with less,” “Open and responsible development,” “Empower innovation,” and “Optimizers” — a research-driven, mission-oriented environment.
- **Remote/Hybrid/Office**: Primarily office-based in San Francisco (251 Rhode Island Street) with employees across 16 countries. Office perks include lunch, dinner, snacks, parking/transit stipends, and relocation support.
- **Benefits**: Competitive salary and equity, 401K matching, health/dental/vision insurance, flexible PTO, generous parental leave, life and disability protection.
- **Engineering Culture**: Deep focus on systems research and open-source; engineers work alongside leading AI researchers (e.g., Tri Dao, Ce Zhang). Talent sourced from Apple, Google, Amazon, NVIDIA, Stanford AI Lab.
- **Career Growth**: 53 active job postings (as of mid-2026); roles span ML engineering, platform engineering, research, and go-to-market. High growth trajectory offers rapid advancement.

## Sources
1. [together.ai/about-us](https://www.together.ai/about-us)
2. [together.ai/](https://www.together.ai/)
3. [together.ai/careers](https://www.together.ai/careers)
4. [linkedin.com/company/togethercomputer](https://www.linkedin.com/company/togethercomputer)
5. [cbinsights.com/company/together-4](https://www.cbinsights.com/company/together-4)

## Other roles at Together AI

- [Sr. Field Marketing & Community Manager](https://feeny.ai/job/sr-field-marketing-community-manager-together-ai-new-york-ktvpck67xssg) — New York, NY
- [Sr. Field Marketing & Community Manager, EMEA](https://feeny.ai/job/sr-field-marketing-community-manager-emea-together-ai-london-zv31yqscs4zq) — London, United Kingdom
- [Event & Field Marketing Specialist](https://feeny.ai/job/event-field-marketing-specialist-together-ai-san-francisco-nge6b7exjfwk) — San Francisco, CA
- [(Senior/Junior AI Inference Reliability Engineer, Compute Infra](https://feeny.ai/job/senior-junior-ai-inference-reliability-engineer-compute-infra-together-ai-san-ezx2bg8b6zje) — San Francisco, CA
- [Senior Recruiter, G&A](https://feeny.ai/job/senior-recruiter-g-a-together-ai-san-francisco-h3zvr47skd5r) — San Francisco, CA
- [Technical Writer Intern, Developer Experience (Summer 2027)](https://feeny.ai/job/technical-writer-intern-developer-experience-summer-2027-together-ai-san-sahqq9k7yvzx) — San Francisco, CA
- [Director, Debt Capital Markets](https://feeny.ai/job/director-debt-capital-markets-together-ai-san-francisco-7ryc68dwe7h0) — San Francisco, CA
- [Growth Marketing Intern (Summer 2027)](https://feeny.ai/job/growth-marketing-intern-summer-2027-together-ai-san-francisco-m075apmcwsef) — San Francisco, CA
- [Senior Counsel - Product & Privacy](https://feeny.ai/job/senior-counsel-product-privacy-together-ai-san-francisco-ng1h3afr2n1z) — San Francisco, CA
- [(Staff / Senior Staff) AI Inference Reliability Engineer, Compute Infra](https://feeny.ai/job/staff-senior-staff-ai-inference-reliability-engineer-compute-infra-together-ai-2j57agj7vfjh) — San Francisco, CA