Chief technology officer in AI

Toptal is a global network of top talent in business, design, and technology that enables companies to scale their teams, on-demand. With $200+ million in annual revenue and team members based around the globe, Toptal is the world’s largest fully remote workforce. As Chief Technology Officer, you will drive execution for the product and engineering teams and put AI at the core of everything the company builds.

• You will own Toptal’s full technical architecture and you will build it AI-first—utilizing best practices for agentic engineering.
• You will create and own the AI architecture and best practices the rest of the company utilizes.
• You will also own the initiatives that put agents into production.
• You will set the technical direction and hold the cadenced meetings that turn technical strategy into working, production-ready outcomes.
• You are the technical owner of all Toptal’s products.
• You will collaborate closely with Product to design infrastructure that exposes Toptal’s core business logic through modular interfaces the rest of the business can build on.
• You will own resourcing and workforce planning as a doer: scoping the work, sizing the teams, planning headcount against it, defining the role and the bar for every seat.
• Then, you lead the team. You will set the standards, develop people against those standards, and know exactly who is meeting those standards.
• You will coach engineers on agentic best practices, show the executive team what is newly possible with the new AI tooling, and push AI into every function at Toptal.
• You will form your own view of where engineering and AI should go, build the case, and bring the CEO a strategy to react to, not a request for direction.
• Own Toptal’s AI technology strategy and the architecture beneath it: model selection and fine-tuning, harness design, orchestration, retrieval, context engineering, tool and MCP integration, guardrails, evaluation, and cost control, exposed as primitives every team composes against.
• Deploy agentic engineering at scale against the work that runs this business: matching, vetting, onboarding, delivery, and internal operations. Own the outcomes those agents are accountable for.
• Make AI quality measurable. Own the evals, the regression suites, and the review loops, so quality here is proven rather than claimed.
• Rebuild how the platform gets built and operated, raising output per engineer through AI-assisted development, review, testing, and operations.
• Own resourcing and workforce planning: scope the roadmap into buildable work, plan headcount and sequence, write the specs, and hit time-sensitive hiring commitments.
• Own the bar for the team: assess talent honestly, develop people against the bar, and hold it as the organization grows.
• Own the architecture end-to-end, from services and data flows to interfaces and system boundaries, and hold the standards that keep it coherent as it grows.
• Own throughput, reliability, and the platform layer beneath them, including the AI-dependent paths where latency, drift, and provider outages are real failure modes.
• Own build-versus-buy, technical debt, secure-by-design architecture, responsible AI, and the engineering and AI cost structure down to inference and token spend.

• Bachelor’s degree in Computer Science, Engineering, or a related technical field is required.
• 15+ years of progressive engineering leadership, including significant time as a CTO or VP of Engineering, or as a direct report to one at meaningful scale.
• Deep, current, hands-on command of modern AI engineering: foundation models, agentic architectures, harness design, retrieval, context engineering, fine-tuning, and evaluation.
• You have shipped and operated these in production, and you know how to build and fine-tune foundation models from the ground up.
• Agentic systems deployed at scale against real business processes, with measured outcomes on cost, cycle time, quality, or revenue, and a record of retiring what did not work.
• Rigor about evaluation. You can explain precisely how you knew a model, prompt, or agent change was an improvement, and you can show the evidence.
• Command of the economics underneath it: model routing, hosted versus self-hosted, inference capacity, latency and cost budgets, and staying portable across providers.
• Further ahead on AI than the executives around you, able to teach it, and proven at moving an organization to adopt something it was not asking for.
• You create strategy rather than administer it, and you win arguments on evidence rather than authority, including your view on what a team should look like in the age of AI.
• Proven ownership of resourcing and workforce planning, and a hiring track record you can quantify. You have made the hard calls when someone was not clearing the bar.
• Currently hands-on. You read and write code, you can take apart a design document or an eval harness and find the flaw in it, and you command distributed systems, service architecture, data-intensive applications, and cloud infrastructure at scale.
• Experience scaling and modernizing a large existing codebase without stopping the business. You have replaced load-bearing systems in flight, not only built greenfield.
• Experience leading a globally distributed, remote team at a technology or hyper-growth company is required.
• Outstanding written and verbal communication skills.
• Speed, ownership, and quality. You move with urgency, decide without perfect information, and treat everything you deliver as a reflection of yourself.
• You must be a world-class individual contributor to thrive at Toptal. You will not be here just to tell other people what to do.
• Будет плюсом: A high-volume, two-sided marketplace or multi-tenant platform is a significant plus

• This is a remote position.