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About the Role
As a Forward Deployed Engineer (FDE) focused on Inference & Post-Training, you will be a hands-on technical partner to strategic customers, specializing in inference optimization, fine-tuning pipelines, and production deployment. You will ensure successful platform adoption, guide tailored optimization efforts, and impact customer success and company growth.
Responsibilities
- Select, configure, and optimize inference engines based on hardware, model architecture, and workload profile.
- 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.
- Drive hands-on RL training runs and optimize system design.
- Guide customers through LoRA, SFT, DPO, RLHF, and GRPO pipelines from experimentation through production.
- Act as the primary technical point of contact for strategic accounts, monitoring and optimizing endpoint configurations.
- Help customers maximize platform utilization and collaborate to ensure critical milestones are met.
- Establish direct alignment with strategic customers at onboarding to ensure optimal inference and post-training configurations from day one.
- Influence the software and model roadmap by surfacing insights from the field.
- Contribute back to the product to support customer requirements or drive a better experience.
- Drive early feature and research adoption with strategic logos.
Requirements
- 5+ years in a technical role, with a strong focus on inference systems, open-source LLM deployment, or post-training workflows.
- 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.
- Deep knowledge of KV cache tuning, speculative decoding, tensor parallelism, pipeline parallelism, and quantization techniques.
- Hands-on experience with fine-tuning and post-training pipelines, including LoRA, SFT, DPO, RLHF, and GRPO.
- Ability to advise on system design.
- 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.
- Strong Python skills.
- Comfortable working in production environments.
Skills
- Inference Engine Optimization
- Configuration & Performance Tuning
- KV cache tuning
- Speculative decoding
- Tensor parallelism
- Quantization strategy
- Post-Training & Fine-Tuning
- LoRA
- SFT
- DPO
- RLHF
- GRPO
- System design
- Python
Work Type
- Remote
Experience Level
- 5+ years
Salary/Compensations
- $270,000 - $300,000 OTE
Benefits
- Startup equity
- Health insurance
- Other benefits
About the Company
- Together AI is a research-driven artificial intelligence company.
- We believe open and transparent AI systems will drive innovation and create the best outcomes for society.
- Our mission is to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models.
- We have contributed to leading open-source research, models, and datasets to advance the frontier of AI.
- Our team has been behind technological advancements such as FlashAttention, Hyena, FlexGen, and RedPajama.
- Join a passionate group of researchers building the next generation of AI infrastructure.
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.