About Elastix AI

We are building the next-gen AI inference platform.

Description

Job Title: Software Engineer, AI Inference Platform

Company: ElastixAI, Inc.

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