Forward Deployed Engineer

Forward Deployed Engineer
As a Forward Deployed Engineer (FDE) focused on Inference & Post-Training, you will serve as a hands-on technical partner to strategic customers building and deploying AI applications at scale. This role complements Solutions Architecture by providing deep expertise in inference optimization, fine-tuning workflows, and production deployment of large language models.
Working across Customer Success, Engineering, and Go-to-Market teams, you will help customers successfully evaluate, deploy, and optimize AI solutions. You will play a critical role in delivering successful proof-of-concepts, accelerating platform adoption, and influencing product development through direct customer engagement.
Key Responsibilities
Inference Optimization
Select, configure, and optimize inference frameworks based on hardware environments, model architectures, and workload requirements.
Improve performance across customer deployments by tuning system configurations to meet throughput, latency, and efficiency objectives.
Performance Engineering
Support critical customer evaluations and production deployments through hands-on performance tuning.
Optimize techniques such as KV cache management, speculative decoding, parallelism strategies, and quantization approaches.
Fine-Tuning & Post-Training
Design, execute, and optimize post-training workflows.
Advise customers on fine-tuning methodologies, including supervised fine-tuning, parameter-efficient training, preference optimization, reinforcement learning-based approaches, and production deployment best practices.
Customer Partnership
Act as a trusted technical advisor for strategic accounts.
Collaborate closely with customer teams to optimize deployments, troubleshoot challenges, and ensure successful achievement of key milestones.
Guide customers through initial platform adoption and ensure optimal inference and training configurations are established from the outset.
Influence product direction by bringing customer feedback, feature requests, and operational insights back to internal engineering and research teams.
Support the adoption of emerging platform capabilities and experimental features with key customers.
Experience
5+ years in technical roles related to machine learning infrastructure, model deployment, inference systems, or AI platform engineering.
Deep hands-on experience with modern LLM inference frameworks and the ability to diagnose performance bottlenecks and deployment issues.
Strong understanding of model-serving optimization techniques, including caching strategies, parallelism approaches, quantization, and low-latency inference design.
Practical experience implementing and supporting fine-tuning and post-training workflows, including parameter-efficient adaptation and reinforcement learning methodologies.
Strong understanding of the open-source model landscape and experience selecting appropriate models based on use case, hardware constraints, and performance objectives.
Strong Python development skills and experience operating in production environments.

Work arrangement
No