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

Source, engage, and nurture passive candidates for business-side roles across go-to-market, infrastructure strategy, and G&A. Partner with recruiters and hiring managers to define candidate profiles, craft personalized outreach, build talent pipelines, track sourcing metrics, and develop scalable sourcing systems. The role requires managing senior, hard-to-fill searches in a startup or hyper-growth environment while adapting across functions and using recruiting tools such as GEM, LinkedIn Recruiter, Noon AI, and Greenhouse.

Sources and evaluates elite engineering candidates through proactive outreach, GitHub review, and technically deep screening calls. Builds talent pipelines, runs outreach campaigns, assesses candidates’ technical depth and ownership, and partners with hiring managers and recruiters to calibrate hiring standards. The role requires an engineering background, strong technical fluency, persistence, and compelling written communication. Recruiting experience is optional and can be learned on the job.

Own investor relationships, equity fundraising, investor communications, and the company’s investment narrative. Lead board reporting, long-range planning, valuation and fundraise models, KPI development, investor targeting, diligence, and executive preparation. Translate AI infrastructure economics, financial performance, market trends, and investor sentiment into clear strategic insights for leadership, board members, and investors.

Leads Together AI’s partner marketing function by defining strategy, messaging, and co-marketing execution with NVIDIA and cloud partners. Collaborates with GTM, product marketing, demand generation, and field marketing teams to deliver campaigns, establish partner operating rhythms, track performance metrics, and improve ROI. The role requires building the function from the ground up and scaling partner go-to-market programs for a B2B AI or software platform business.

Lead platform infrastructure strategy and build reusable service infrastructure across Kubernetes, AWS, networking, and operational systems. Develop Helm charts, Terraform modules, CI/CD, GitOps workflows, zero-trust policies, and self-service platform capabilities. Partner with service, SRE, security, networking, and infrastructure teams to modernize services and drive adoption of shared patterns. Lead cross-company infrastructure initiatives and establish an initial platform infrastructure team.

Coordinates recruiting activities from interview scheduling through offers, background checks, onboarding transitions, and candidate communication. Maintains ATS records, prepares recruiting metrics, supports hiring managers and interviewers, improves recruiting processes, and ensures a positive candidate experience. Requires strong organization, communication, adaptability, confidentiality, and familiarity with recruiting tools and office productivity software.

Own full-cycle recruiting for go-to-market and business teams in a high-growth AI company. Partner with executives to define hiring strategies, candidate profiles, and interview processes; manage candidates through offer close; provide market intelligence and funnel metrics; and improve recruiting workflows and candidate experience. The role requires strategic leadership partnership, adaptability in ambiguity, and experience building recruiting processes from the ground up.

Lead architecture and implementation of Together AI’s global GPU cloud infrastructure. Own GPU and network virtualization, in-data-center IaaS, distributed scheduling and management planes, fault-tolerant automation, and customer-facing production systems. Set technical direction across teams, mentor engineers, establish engineering standards, and build scalable services spanning Kubernetes, compute, storage, networking, and GPU infrastructure.

Conduct research on frontier AI agents, including alignment, reliability, long-horizon reasoning, dataset design, evaluation, safety, and scalable infrastructure. Develop novel techniques, run rigorous experiments, document findings in publications and blog posts, and communicate project progress. Work may involve machine learning, deep learning, reinforcement learning, spoken-input agents, and foundation model development.

Develop and optimize GPU-accelerated kernels and algorithms for AI and machine learning applications. Collaborate with modeling, algorithm, hardware, and software teams on GPU architecture and programming-model co-design. Integrate GPU solutions into software systems, optimize performance and scalability, and track advances in GPU programming techniques. The internship lasts 12–14 weeks and includes cross-functional research and engineering work.