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Nebius

Runs GPU clouds for AI training and deployment

Field CTO – Financial Services AI Infrastructure

Pre-Sales Solution EngineeringConfirmed live in the last 24 hours
Full-timeRemote in USA$210k - $260k

About the Job

Requirements

  • 10+ years of experience in cloud infrastructure, platform engineering, distributed systems, or a closely related technical domain, including 5+ years in AI, machine learning, or MLOps with exposure to production or large-scale systems.
  • Prior experience in a senior technical leadership role, such as Field CTO, CTO or technical co-founder of a fintech or AI-native company, head of quant or ML platform, or principal-level architect on a cloud vendor financial services team.
  • Ability to explain distributed systems to engineers and communicate their value and risk profile to CIOs or Chief Risk Officers.
  • Experience partnering with Sales to close large enterprise deals and enabling field teams with technical assets.
  • Experience defining requirements from field evidence and shaping platform strategy, including evaluating customer requests and proposing alternatives that avoid technical debt.
  • Expert-level, production-grade knowledge of GPU architectures, Kubernetes and Slurm orchestration including Soperator, high-performance and parallel file systems, and networking constraints including InfiniBand and Ethernet.
  • Knowledge of model serving, including batch sizes, quantization, KV caching, and latency tradeoffs, and ability to architect for both high throughput and latency-sensitive workloads such as real-time risk and fraud scoring.
  • First-hand experience in capital markets and quantitative research infrastructure, bank or insurer AI platforms, AI-native fintech, or an FSI technical role at a cloud or GPU provider, plus working knowledge of regulated-buyer vendor reviews, data residency, third-party and concentration risk, and model risk management expectations such as SR 11-7 in the US.
  • Ability to form and pursue a point of view on the future of AI infrastructure in financial institutions and to start as a hands-on individual contributor.
  • Public technical credibility demonstrated through conference talks, writing, or open-source work.
  • Ability to influence product teams and leadership through clear, credible communication and executive presence.
  • Experience operating in fast-moving environments such as hyperscalers, cloud platforms, or advanced AI startups where requirements are evolving.

Responsibilities

  • Serve as the technical face of Nebius to financial services customers, partners, and ecosystem stakeholders.
  • Lead architecture workshops and technical discovery with CTOs, heads of quant and ML platforms, and CIOs, demonstrating performance on customer workloads and fit with institutional control frameworks.
  • Partner with Sales leadership on strategic financial services deals, including workload-level cost comparisons against on-premises and hyperscaler options.
  • Work with Security, GRC, and Legal to translate SOC 2 Type II and ISO 27001 certifications and alignment with frameworks such as DORA into answers accepted by third-party risk teams.
  • Address data residency, tenant isolation, key management, audit logging, resilience evidence, concentration risk, and exit and portability plans; flag gaps early and bring them to Product as requirements.
  • Define reference architectures for quantitative research and backtesting, real-time risk and fraud inference, LLM and agentic workflows in KYC, AML, and document processing, and fine-tuning on sensitive proprietary data.
  • Translate account learnings into prioritized, evidence-backed requirements for Product, Engineering, and Infrastructure teams and maintain a structured feedback loop for R&D.
  • Create the technical narrative, reference architectures, and training materials that help Field SAs and sellers engage financial services buyers credibly.
  • Partner with ecosystem vendors including market and reference data providers, model risk and AI governance tooling, trading and risk platforms, and systems integrators to identify integration opportunities and evaluate potential technical M&A targets.
  • Build and lead forward-deployed engineers as the vertical grows, setting how they scope and deliver work and share learnings with Product.

Desired Qualifications

  • Hands-on experience with quantitative research infrastructure, low-latency trading systems, or model risk management in a regulated institution.
  • Familiarity with hybrid and on-premises GPU environments used by financial institutions.
  • Ability to distinguish customers who need raw cluster access from those who need managed endpoints and prescribe an appropriate path.

About the Company

Nebius

Runs GPU clouds for AI training and deployment

Nebius runs an AI cloud for developers, researchers, and enterprises that train models and put them into production. Its AI Cloud combines GPU computing, storage, networking, and managed services, while Token Factory runs models through an API so teams do not manage the underlying servers. Customers rent computing and storage at usage rates or reserve capacity, choosing resources that fit short experiments or sustained production workloads. Beyond the cloud, the group owns Avride, which develops autonomous vehicles and delivery robots, and TripleTen, which teaches technical skills for people changing careers. The publicly listed group is headquartered in Amsterdam, Netherlands.

Company size
1,001-5,000
Company stage
IPO
Headquarters
Amsterdam, Netherlands
Founded
1997

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