Principal Software Engineer

Technical Leadership & Architecture
• Define and drive the technical vision and long-term architecture strategy for AI/MLplatforms, model training/serving infrastructure, and large-scale data systems
• Lead the design of next-generation AI infrastructure including model training pipelines,feature stores, real-time inference systems, and GPU/accelerator cluster management
• Make high-impact design decisions on distributed systems that handle millions of QPSand petabyte-scale data processing
• Establish and champion engineering best practices for AI system reliability, performanceoptimization, and cost efficiency
AI/ML Systems & Infrastructure
• Architect and build scalable ML platforms that accelerate model development, training,evaluation, and deployment lifecycles
• Design high-performance inference serving systems with low-latency requirements forreal-time AI applications
• Drive innovation in LLM (Large Language Model) infrastructure, including fine-tuning,RLHF pipelines, and efficient serving frameworks
• Lead the development of foundational infrastructure components: container orchestration,service mesh, observability, and CI/CD for ML workloads
• Optimize compute resource utilization across heterogeneous hardware (GPU, TPU,custom accelerators)
Execution & Delivery
• Hands-on contribution to the most complex and ambiguous technical challenges in AI andinfrastructure
• Own end-to-end delivery of large-scale, cross-team projects from concept to production
• Drive technical feasibility assessments for new AI product initiatives and infrastructureinvestments
• Identify and resolve systemic technical risks, performance bottlenecks, and reliabilityconcerns
Influence & Mentorship
• Partner with senior leadership (VP/Director level) to align AI/infrastructure strategy withbusiness objectives
• Drive cross-functional collaboration across AI research, engineering, product, andplatform teams
• Mentor and coach senior engineers, fostering a culture of technical excellence andinnovation
• Raise the engineering bar through design reviews, code reviews, and architecturalguidance
• Influence the broader tech community through open-source contributions, publications, orconference talks

🌍 Global Reach
Open to global tech talent, not geo-restricted
Explicitly requires experience in globally distributed teams across time zones
Remote/hybrid options — very appealing to international candidates
🤖 AI + Infrastructure Dual Focus
Rare intersection role: not pure AI research, not pure infra — the bridge between both
Covers the hottest areas: LLM infrastructure (training, fine-tuning, RLHF, efficient serving)
GPU cluster management & heterogeneous compute (A100/H100, CUDA, NCCL) — seriously hard-core