Top Tech Jobs & Startup Jobs in Chicago, IL

Senior Software Engineer on the Azure Core New Tech team to design and implement software that automates verification, onboarding, tracking, and lifecycle management of hardware for Microsoft datacenters. Collaborate with stakeholders, create designs and playbooks, write and refactor code, operate on-call monitoring and incident response, and drive improvements in reliability, observability, performance, and security for scalable distributed systems.

Lead technical ownership of InfiniBand and GPU interconnect fabric operations for hyperscale AI supercomputers. Drive incident response, deep multi-layer debugging across hardware/firmware/drivers/OS, and define failure models, playbooks, telemetry, and automation to improve reliability, observability, and scalability of GPU clusters and platform SLAs.

Lead vision, roadmap, and cross-functional teams to build AI-driven skilling products. Define success metrics, prioritize investments, ship LLM-powered features, measure learning outcomes, and drive adoption through partner and go-to-market collaboration.

Lead customer-facing delivery of enterprise AI solutions as product owner and technical program manager. Translate business needs into prioritized backlogs, run parallel workstreams, provide architecture-aware technical leadership, ensure telemetry and validation, manage governance/release readiness, and scale reusable delivery patterns across engagements.

Manage and coach a team of datacenter trainers to design and deliver technical workforce training, assess learning needs, close operational training gaps, partner with site managers, and align training with availability, safety, and throughput goals across global datacenters.

Design and build evaluation infrastructure for generative AI on large GPU clusters. Develop and tune pretraining software, benchmark NVIDIA and AMD GPU clusters, collaborate with model scientists, implement evaluation methods and inference strategies, and inform compute roadmap and architectural decisions to support Copilot and consumer AI products.

Build and optimize inference systems and tools for generative AI models, collaborate with researchers to improve performance, profiling, debugging, and deploy large-scale distributed inference solutions.

Design and implement algorithms, model architectures, data mixtures, and scaling laws for large-scale foundation model training. Run and oversee large distributed training experiments, collaborate with infrastructure, data, and multimodality teams, and apply rigorous, data-driven ablations to drive flagship training runs and system improvements.

Contribute to building and scaling ML development infrastructure for Copilot: design and optimize CI/CD for large-scale training, build developer tools, maintain multi-cloud infrastructure, and deploy/host models for inference and data generation while collaborating with cross-functional teams to improve reliability, performance, and model development workflows.

Design, implement, and optimize distributed pre-training infrastructure for large-scale GPU clusters. Profile, benchmark, and debug compute, memory, networking, and storage bottlenecks; optimize communication libraries and GPU programming stacks; collaborate with hardware and model teams to scale training, ensure reliability, and influence architecture and roadmap.