What are the responsibilities and job description for the AI Software Engineer position at ElastixAI?
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