Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Designs and evolves large-scale microservices ecosystems using cloud-native platforms and distributed systems patterns. Leads service decomposition, domain-driven design, distributed data management, resilience, observability, security, API and event-driven architecture. Operates high-traffic production systems, guides platform engineering and cloud cost optimization, and leads architecture initiatives across multiple teams while managing stakeholders.

Designs, builds, and operates secure, highly available AWS cloud platforms and production workloads. Responsibilities include AWS architecture, infrastructure as code, automation, Kubernetes or ECS operations, CI/CD, cloud security, observability, troubleshooting, cost optimization, and continuous operational improvement. The engineer collaborates with application, security, and SRE teams to deliver resilient, scalable cloud-native solutions.

Optimize large-scale AI training and inference systems for throughput, latency, and cost. Responsibilities include GPU kernel and compiler optimization, distributed system tuning, memory and parallelism optimization, profiling across CPU and GPU systems, model compression, and production-scale LLM inference improvements. The role requires rigorous measurement, debugging, cross-functional collaboration, code and design reviews, mentorship, and delivery of reliable production engineering solutions.

Design, build, and operate reliable, high-performance inference platforms for large machine learning models in production. Responsibilities include request routing, batching, caching, autoscaling, GPU utilization, observability, distributed systems, performance engineering, capacity planning, incident response, and cost-efficient serving across diverse workloads.

Provides technical leadership across teams, drives complex engineering initiatives, shapes architecture, and executes technical roadmaps. Responsibilities include translating ambiguous requirements into scalable solutions, conducting code and design reviews, mentoring engineers, improving reliability and observability, and influencing engineering standards and culture. The role requires deep expertise in distributed systems, cloud platforms, or large-scale data systems, along with strong backend programming, system design, communication, and cross-functional collaboration skills.

Develop and optimize CUDA-based workloads for AI, scientific computing, inference, and high-throughput processing. Design custom GPU kernels, profile production workloads, integrate kernels into ML frameworks, and improve performance across modern accelerator platforms. Collaborate with engineering and research teams, participate in code and design reviews, translate requirements into production solutions, and mentor junior engineers.

Designs and operationalizes fine-tuning workflows for large language models using supervised, preference-based, and reinforcement learning. Responsibilities include dataset construction, evaluation methodology, distributed training, GPU cluster operations, failure recovery, and production pipeline reliability. The engineer collaborates with cross-functional teams, ships impactful LLM solutions, conducts design and code reviews, and mentors junior engineers.

Designs, deploys, and operates secure, scalable Microsoft Azure infrastructure for production workloads. Responsibilities include cloud architecture, infrastructure as code, automation, AKS operations, CI/CD, security hardening, cost optimization, monitoring, observability, troubleshooting, and compliance. The engineer partners with application development, security, and SRE teams to deliver resilient cloud-native platforms.

Designs and builds end-to-end Industrial IoT systems connecting factory-floor devices to enterprise cloud platforms. Responsibilities include edge data acquisition, secure connectivity, cloud ingestion, real-time analytics, operational dashboards, OT/IT integration, troubleshooting, code and design reviews, production delivery, and mentoring junior engineers. The role requires programming, industrial protocol, cloud IoT, edge runtime, streaming analytics, and industrial cybersecurity expertise.

Design, train, evaluate, and deploy reinforcement learning systems for complex decision-making problems. Develop reward functions, simulation environments, and neural network policies; scale training on GPU clusters; and move RL solutions from research into reliable production systems. The role requires strong theoretical and engineering expertise, with preferred experience in RLHF, multi-agent or hierarchical RL, robotics, autonomous driving, and open-source or published RL work.