AI Software Engineer

About the role

A elastixAI is an early-stage startup building the next-generation AI inference infrastructure — co-designed across ML software and custom accelerator hardware. Our platform dynamically optimizes inference efficiency and scalability across diverse deployments, enabling adaptive, high-performance AI serving.

Role Summary

We’re looking for a systems-minded AI Software Engineer to join our core inference platform team. You’ll design and extend the low-level serving stack — hacking open-source frameworks like vLLM, SGLang, and TensorRT-LLM, building new model sharding and scheduling logic, and integrating deeply with our proprietary AI accelerator.

Key Responsibilities

• Architect, extend, and optimize core components of our AI serving platform for throughput, latency, and scalability.
• Customize open-source serving frameworks (e.g., vLLM) for proprietary model ingestion and accelerator integration.
• Develop efficient model partitioning, scheduling, and memory management strategies for multi-device inference.
• Collaborate with ML engineers on model export and runtime optimization (quantization, graph transforms).
• Work closely with hardware engineers to influence accelerator interface design and performance tuning.
• Build APIs and runtime tools enabling flexible, PyTorch-native model deployment on our infrastructure.
• Profile, debug, and optimize across the full stack — from Python orchestration to C++ kernels and PCIe drivers.

Required Qualifications

• BS/MS/PhD in Computer Science, Electrical/Computer Engineering, or related field.
• 3+ years of professional experience in systems programming, ML infrastructure, or distributed inference.
• Proficient in C++ and Python, with strong debugging and performance analysis skills.
• Deep familiarity with one or more LLM serving frameworks (vLLM, SGLang, TensorRT-LLM, DeepSpeed-Inference, etc.).
• Understanding of model deployment internals — token scheduling, KV caching, batching, and pipelined inference.
• Comfortable working close to the hardware abstraction layer — CUDA, PCIe, memory management, or runtime scheduling.
• Strong collaboration and communication skills; ability to work cross-functionally in a fast-paced startup environment.

Preferred / Bonus

• Experience with hardware-aware ML optimization, compiler/runtime integration, or accelerator SDKs.
• Hands-on experience profiling GPU/accelerator workloads.
• Familiarity with containerized deployments (Docker/Kubernetes).
• Exposure to distributed systems or large-scale inference clusters.
• Contributions to open-source ML or serving frameworks.