Senior Backend Engineer, Inference Platform

About the Role

Together AI is building the Inference Platform that brings the most advanced generative AI models to the world. Our platform powers multi-tenant serverless workloads and dedicated endpoints, enabling developers, enterprises, and researchers to harness the latest LLMs, multimodal models, image, audio, video, and speech models at scale.

If you get a thrill from optimizing latency down to the last millisecond, this is your playground. You'll work hands-on with tens of thousands of GPUs (H100s, H200s, GB200s, and beyond), figuring out how to fully utilize every FLOP and every gigabyte of memory.

You'll collaborate directly with research teams to bring frontier models into production, making breakthroughs usable in the real world. Our team also works closely with the open source community, contributing to and leveraging projects like SGLang, vLLM, and NVIDIA Dynamo to push the boundaries of inference performance and efficiency.

• Shape the core inference backbone that powers Together AI's frontier models.
• Solve performance-critical challenges in global request routing, load balancing, and large-scale resource allocation.
• Work with state-of-the-art accelerators (H100s, H200s, GB200s) at global scale.
• Partner with world-class researchers to bring new model architectures into production.
• Collaborate with and contribute to the open source community, shaping the tools that advance the industry.
• A culture of deep technical ownership and high impact - where your work makes models faster, cheaper, and more accessible.
• Competitive compensation, equity, and benefits.

Responsibilities

• Build and optimize global and local request routing, ensuring low-latency load balancing across data centers and model engine pods.
• Develop auto-scaling systems to dynamically allocate resources and meet strict SLOs across dozens of data centers.
• Design systems for multi-tenant traffic shaping, tuning both resource allocation and request handling - including smart rate limiting and regulation - to ensure fairness and consistent experience across all users.
• Engineer trade-offs between latency and throughput to serve diverse workloads efficiently.
• Optimize prefix caching to reduce model compute and speed up responses.
• Collaborate with ML researchers to bring new model architectures into production at scale.
• Continuously profile and analyze system-level performance to identify bottlenecks and implement optimizations.

Requirements

• 5+ years of demonstrated experience building large-scale, fault-tolerant, distributed systems and API microservices.
• Strong background in designing, analyzing, and improving efficiency, scalability, and stability of complex systems.
• Excellent understanding of low-level OS concepts: multi-threading, memory management, networking, and storage performance.
• Expert-level programming in one or more of: Rust, Go, Python, or TypeScript.
• Knowledge of modern LLMs and generative models and how they are served in production is a plus.
• Experience working with the open source ecosystem around inference is highly valuable; familiarity with SGLang, vLLM, or NVIDIA Dynamo will be especially handy.
• Experience with Kubernetes or container orchestration is a strong plus.
• Familiarity with GPU software stacks (CUDA, Triton, NCCL) and HPC technologies (InfiniBand, NVLink, MPI) is a plus.
• Bachelor's or Master's degree in Computer Science, Computer Engineering, or related field, or equivalent practical experience.

Compensation

We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $200,000 - $290,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.