AI Software Engineer

Location:

 Seattle, WA (Hybrid - 3 days/week in office)

**** About ElastixAI

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 . This role sits at the intersection of  ML systems, compiler/runtime engineering, and hardware-software co-design .

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.

What We Offer:

• A chance to be a foundational engineer in an innovative AI startup
• A dynamic and collaborative work environment and the change to have a significant impact on new technology
• The opportunity to work on challenging problems at the intersection of ML, software, and systems.
• Competitive compensation and startup equity package
• Comprehensive medical, dental, and vision coverage (100% paid by employer)
• Life insurance and AD&D
• Flexible Time Off (FTO)
• 12-paid holidays
• Paid parental leave
• Gym or fitness benefit
• Commuter benefit
• Weekly catered lunches in the office
• Investment in employee learning & development