Software Engineer, AI Inference Platform

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., * 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.

• 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., * 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.

• 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