Top Tech Jobs & Startup Jobs

Lead strategy and tooling for prompt design, agentic workflows, and LLM-based architectures. Build reusable prompt patterns, evaluation frameworks, and developer tooling. Ship LLM products to production, ensure responsible AI practices, and mentor engineers.

Design, execute, and operationalize fine-tuning workflows for large language models using supervised, preference-based, and reinforcement learning approaches. Build and operate distributed training pipelines, construct datasets, run rigorous evaluations, and collaborate with cross-functional teams to deliver production-grade LLM solutions while mentoring junior engineers.

Lead enterprise architecture and long-term technology strategy to design and deliver highly scalable, resilient distributed systems. Serve as technical authority across teams, drive enterprise-scale architectural decisions, mentor senior engineers, and improve system performance, reliability, and cross-team engineering practices.

Design, train, evaluate, and deploy reinforcement learning systems for production decision-making. Build and scale RL training pipelines, design reward functions, work with simulation environments and GPU clusters, and ensure stability, safety, and ongoing improvement of RL policies.

Design and build software tooling, firmware, and infrastructure supporting semiconductor design, verification, manufacturing, and post-silicon validation. Partner with hardware and EDA teams to accelerate silicon development, improve yield and performance, and deliver scalable compute, automation, and data-pipeline solutions. Mentor junior engineers and drive code and design quality.

Design, build, and operate high-performance inference platforms for large ML models, focusing on request routing, batching, caching, autoscaling, GPU utilization, observability, and performance trade-offs to deliver low-latency, high-throughput production serving.

Design, deploy, and operate large-scale, secure Azure cloud platforms. Own architecture, infrastructure-as-code, automation, AKS operations, CI/CD, security hardening, cost optimization, and observability for production workloads while partnering with development, security, and SRE teams.

Optimize throughput, latency, and cost for large-model training and inference across the stack, from kernel and compiler-level work to distributed system tuning. Profile and instrument GPU and distributed workloads, apply model-parallelism and memory-management strategies, and implement model-compression tradeoffs. Collaborate with cross-functional teams, perform code and design reviews, mentor junior engineers, and ship production-quality ML infrastructure improvements.

Design and ship production-ready machine learning systems by bridging applied research and engineering. Responsibilities include model training, fine-tuning, evaluation at scale, distributed and mixed-precision training, deployment with observability and safeguards, and adapting current research for high-impact production use.

Design and govern enterprise-grade application architectures across cloud, integration, and data domains. Partner with stakeholders to translate strategy into scalable, secure solutions; produce architecture artifacts; lead reviews; influence engineering direction; and optimize cost, scalability, and resilience across large application portfolios.