About the Role

Key Responsibilities

Program Execution & Delivery

• Drive end-to-end execution of large-scale, cross-functional programs spanning inference engines (e.g., SGLang), training frameworks (e.g., Miles), and hardware integration efforts.
• Define program structure, including milestones, dependencies, critical paths, risks, and success criteria. Maintain a clear source of truth for status across all stakeholders.
• Run design reviews, sprint planning, release readiness reviews, and post-mortems. Ensure decisions are documented and follow-ups are closed out.
• Identify and unblock cross-team dependencies across kernel, runtime, scheduler, networking, and model teams before they become release blockers.
• Drive release management for major versions, including changelog ownership, compatibility validation, partner rollout sequencing, and rollback planning.

Technical Coordination

• Partner with Product Management to translate roadmap priorities into executable program plans, with clear scope, staffing, and timelines.
• Work shoulder-to-shoulder with engineering leads on technical trade-off decisions; understand the architecture deeply enough to ask the right questions and surface hidden risks.
• Coordinate hardware enablement programs with partners like Nvidia, Google, and AWS, including new accelerator bring-up, kernel co-development, and benchmark validation.
• Manage integration programs with frontier AI labs and early adopters, ensuring technical requirements, SLAs, and feedback loops are well-defined.

Operational Excellence

• Build and improve the engineering operating cadence, including standups, planning rituals, OKR tracking, dashboards, and reporting to leadership.
• Establish metrics and instrumentation for program health such as velocity, defect rates, benchmark regressions, and customer-reported issues, and drive accountability against them.
• Lead incident response coordination for production issues affecting partners; own root-cause review and corrective-action tracking.
• Improve developer productivity by identifying and removing systemic friction in our build, test, and release pipelines.

Stakeholder Communication

• Serve as the connective tissue between engineering, product, GTM, and external partners, ensuring everyone has the right information at the right altitude.
• Produce clear, concise written updates for leadership and partners. Translate engineering progress into business-relevant signals.
• Represent program status honestly, including risks and slips, with concrete mitigation plans.

Qualifications

Minimum Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.
• 4+ years of direct experience in Technical Program Management, Engineering Management, or a senior engineering role with significant program ownership, in a software or infrastructure company.
• Strong technical fluency in systems software, distributed systems, or AI/ML infrastructure; able to read code, follow architecture discussions, and challenge technical assumptions productively.
• Demonstrated track record shipping complex, multi-team programs on time, including managing dependencies, risks, and scope changes.
• Excellent written and verbal communication skills; able to drive alignment across engineers, executives, and external partners.

Preferred (Bonus) Qualifications

• Direct experience shipping AI/ML infrastructure such as inference engines, training frameworks, GPU kernels, distributed schedulers, or model serving platforms.
• Hands-on coding background (Python, C++, CUDA) and comfort working in engineering codebases, including reading PRs, running benchmarks, and reproducing issues.
• Experience coordinating with hardware vendors (Nvidia, AMD, Google TPU, AWS Trainium/Inferentia) on enablement or co-engineering programs.
• Experience driving open-source release programs or working in OSS communities, including issue triage, RFC processes, and contributor coordination.
• Familiarity with release engineering, CI/CD systems, and observability tooling for large-scale distributed systems.
• Experience supporting B2B or developer-facing products with enterprise SLAs.