---
title: 'Forward Deployed Engineer (Inference & Post-Training) - Mandarin Speaking at Together AI'
canonical: 'https://feeny.ai/job/forward-deployed-engineer-inference-post-training-mandarin-speaking-together-ai-s97vm5bghhhz'
type: 'job'
last_seen: '2026-09-11'
---

# Forward Deployed Engineer (Inference & Post-Training) - Mandarin Speaking at Together AI

- **Company:** Together AI
- **Location:** Singapore
- **Work type:** remote
- **Posted:** 2026-08-04
- **Last confirmed live:** 2026-09-11
- **Apply:** https://job-boards.greenhouse.io/togetherai/jobs/5199993007

## Job description

## About the role

As a Forward Deployed Engineer (FDE) focused on Inference & Post-Training, you will be a hands-on technical partner to our most strategic customers — production AI teams looking to leverage high quality models and do inference at scale. For us, FDE is not a replacement for a Solutions Architect; you will partner with our SAs as a deep-domain specialist in inference optimization, fine-tuning pipelines, and production deployment. As key contributors to both the CX, Engineering, and Sales organizations, FDEs add tremendous value by ensuring we can meet the requirements of our most complex POCs, facilitate successful platform adoption, and guide tailored optimization efforts — directly impacting customer success, company growth, and the hardening of our core platform.

Must be a permanent resident or citizen of Singapore.

## Responsibilities

- Inference Engine Optimization: Select, configure, and optimize inference engine based on hardware, model architecture, and workload profile
- Configuration & Performance Tuning: Develop configuration updates to win critical POCs, benchmarks, and optimize customer deployments; tune KV cache, apply speculative decoding, determine optimal tensor parallelism, and determine quantization strategy to hit throughput and latency targets.
- Post-Training & Fine-Tuning: Drive hands-on RL training runs and optimize system design; guide customers through LoRA, SFT, DPO, RLHF, and GRPO pipelines from experimentation through production.
- Strategic Customer Alignment: Act as the primary technical point of contact for aligned strategic accounts — monitoring and optimizing endpoint configurations, helping customers get the most out of the platform, and collaborating to ensure we hit critical milestones.
- Opinionated Onboarding: Establish direct alignment with strategic customers at onboarding; ensure the right inference and post-training configurations are in place from day one to improve time-to-value.
- Product Feedback Loop: Directly influence our software and model roadmap by surfacing insights from the field. Contribute back to the product where needed to support customer requirements or drive a better experience. Drive early feature and research adoption with strategic logos.

## Requirements

- Experience: 5+ years in a technical role, with a strong focus on inference systems, open-source LLM deployment, or post-training workflows.
- Inference Engine Depth: Expert-level, hands-on experience with inference engines (e.g., vLLM, TensorRT-LLM, SGLang); ability to diagnose and resolve performance issues at the engine level.
- Inference Optimization: Deep knowledge of KV cache tuning, speculative decoding, tensor parallelism, pipeline parallelism, and quantization techniques
- Post-Training Knowledge: Hands-on experience with fine-tuning and post-training pipelines, including LoRA, SFT, DPO, RLHF, and GRPO; ability to advise on system design
- Model Landscape Awareness: Broad knowledge of state-of-the-art open-source models and strong judgment on model selection for specific customer use cases, hardware profiles, and performance targets.
- Coding Proficiency: Strong Python skills; comfortable working in production environments

## 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. 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

## 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

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- [Senior ABM & Campaign Manager](https://feeny.ai/job/senior-abm-campaign-manager-together-ai-san-francisco-b4m47ckm79bn) — San Francisco, CA
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