Forward Deployed Engineer, Together AI
Inference & Post-Training
$270-300k
OTE
+ Equity
4 days a week in office
Cloud platform for building generative AI
Be an early applicant
Cloud platform for building generative AI
101-200 employees
Be an early applicant
$270-300k
OTE
+ Equity
4 days a week in office
101-200 employees
Company mission
To empower developers and researchers at organizations of all sizes to train, fine-tune, and deploy generative AI models.
Role
Who you are
- 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
What the job involves
- 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
- 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
Our take
Founded in 2022, Together AI is a research-driven AI company that operates a cloud platform for building and running generative AI. The company also contributes to open-source research, models and datasets to advance the use of machine learning.
Together AI's platform enables developers, researchers and businesses to train, fine-tune and deploy generative AI for their needs. With Generative AI gaining a lot of attention recently, the company has some high-profile competitors such as ChatGPT, but its focus on research makes it stand out.
The company has an incredibly strong start, evidenced by its substantial funding and Unicorn status. It is using these funds to expand its AI Acceleration Cloud product and prepare a large deployment of its GPUs, all while securing its position as an open-source model-building platform.
Steph
Company Specialist at Welcome to the Jungle
Company
Funding (last 2 of 4 rounds)
Feb 2025
$305m
SERIES B
Mar 2024
$106m
SERIES A
Total funding: $533.5m
Company benefits
- Competitive health insurance plans
- Dental and vision insurance
- Pre-tax flexible spending accounts
- Mental health support and services
- Income protection & retirement
- 401(k) plan
- AD&D insurance
- Life insurance
- STD & LTD insurance
- Monthly team lunches
- Flexible time off policy
- Team-driven celebrations and events
- Monthly commuting stipend + pre-tax bene
Company HQ
Design District, San Francisco, CA
Leadership
Ce Zhang
(Co-Founder & CTO)Previously Professor at ETH Zurich, Postdoc Researcher at Stanford University and Research Assistant at University of Wisconsin-Madison.
Vipul Ved Prakash
(Co-Founder & CEO)Previously Senior Director at Apple, CEO and CTO at Topsy and Founder at Cloudmark.
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