toptal - Chief Technology Officer
Requirements
• Bachelor’s degree in Computer Science, Engineering, or a related technical field is required, and an advanced degree is strongly preferred. • 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. A high-volume, two-sided marketplace or multi-tenant platform is a significant plus. • 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.
Responsibilities
• One precondition runs through all of it: you conceive the strategy. 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. • AI and agentic engineering • 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. Engineers have to want this, not comply with it, and creating that pull is on you. • Team, resourcing, and accountability • Decide what a team should look like now that the roles have blurred, where the product manager believes they can design and code, the engineer believes they can do product and design, and the designer believes they can code and do product. • 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. • Platform and execution • 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. • In the first week, expect to: • Learn Toptal’s model and platform architecture, and meet the leaders you will depend on across Engineering, Product, Industries, and Services. • Establish a direct, working rhythm with the CEO. • In the first month, expect to: • Assess the engineering function honestly, and assess AI maturity with it: what is really in production, what is measured, what is duplicated, and what should be shut down. • Assess the team against the bar this role requires, and bring the executive team a strategy no one asked you for. • In the first three months, expect to: • Be executing your own plan, with a review cadence and clear accountability at every level, and your resourcing plan presented. • Stand up the shared AI architecture and evals, and put one agentic workflow into production with a baseline and a measured result. • In the first six months, expect to: • Show business impact from agents in production in at least two core workflows, with the evidence behind it. • Have the leadership team you need, with the bar visibly raised, and throughput and reliability moving on the paths that matter most. • In the first year, expect to: • Run an engineering organization recognized inside and outside Toptal as one of the most advanced in the industry, with the pull on AI now coming from engineering. • Have AI leverage embedded in the architecture and the operating model, not living in a portfolio of pilots, and the team you built delivering against plan.
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