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

Lead complex customer deployments: build, deploy, and customize production software; develop integrations, automation, and tooling; troubleshoot across application and infrastructure layers; translate customer feedback into product requirements; own projects end-to-end and create reusable, scalable deployment solutions while advising customers.

Lead customer-facing technical deployments: build, deploy, and customize production solutions; develop integrations, automation, and tooling; troubleshoot across app and infra layers; translate customer feedback into product requirements; own projects from discovery through iteration and improve deployment processes and reliability.

Lead complex technical deployments for enterprise customers: build, deploy, and customize production solutions; develop integrations, automation, and tooling; troubleshoot across application and infrastructure layers; partner with Product and Engineering; own projects from discovery through deployment and scale reusable solutions.

Build Pragmatike's outbound sales engine: define messaging, prospect, and generate meetings with startup founders, CTOs, and hiring leaders across Europe and North America. Work directly with founders to iterate playbooks, manage your own pipeline, and grow the business in a remote, high-ownership startup environment.

Drive outbound prospecting to generate new sales opportunities for early-stage startups. Build outbound strategy, messaging, and workflows; engage founders, CTOs, and hiring leaders via LinkedIn, email, and cold outreach; manage your pipeline and work closely with founders to convert prospects.

The Principal AI/ML Engineer will lead the design, implementation, and deployment of AI systems, mentor junior engineers, and ensure model performance and reliability in production environments.

Build and maintain production-grade React+TypeScript web interfaces for data-intensive, real-time AI products. Own end-to-end frontend features, optimize UI performance for large datasets, collaborate with design and backend teams, establish frontend best practices, improve architecture and tooling, and resolve production issues across browsers and devices.

The Principal ML Ops Engineer will design, implement, and scale ML infrastructure, focusing on optimizing AI systems, collaborating with teams, and establishing best practices for ML Ops.

You will design, implement, and optimize NVIDIA CUDA kernels for AI systems, improve GPU memory pipelines, and profile workloads to enhance performance.