Forward Deployed Engineer (Inference & 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
- Choose and tune inference engines around customer hardware, model architecture and workload demands
- Adjust KV cache, speculative decoding, parallelism and quantization to meet throughput and latency goals
- Run RL training and guide customers through fine-tuning workflows from early tests to production
- Serve as the technical lead for strategic accounts, improving endpoint setups and keeping customer milestones on track
- Feed field insights into product and research priorities, and help customers adopt new capabilities
What you bring
- At least five years in a technical role focused on inference systems, open-source LLM deployment or post-training
- Hands-on command of inference engines, including diagnosing performance problems inside the engine
- Practical fine-tuning and post-training experience, plus the ability to advise on system design
- Strong Python skills and sound judgment when matching open-source models to customer needs
Who this fits
This role suits a hands-on engineer who can work directly with strategic customers while contributing to engineering, product and sales efforts. The position is based in San Francisco, and the stated US base salary range is $270,000 to $300,000, plus equity and benefits.
From the employer
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.
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.
Qualifications
- 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. 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