Used Tools & Technologies
Machine Learning LLM GenAIRequired Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Kubernetes @ 4
Distributed Systems @ 8
AdTech @ 6
Networking @ 4
Cloud Computing @ 4
GPU @ 4
Generative AI @ 4
AI @ 4
InfiniBand @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
Nebius is leading a new era in cloud computing to serve the global AI economy. We create tools and resources customers need to solve real-world challenges and transform industries without massive infrastructure costs or large in-house AI/ML teams. Nebius is headquartered in Amsterdam with R&D hubs across Europe, North America, and Israel and a team of 800+ employees including 400+ engineers.
This Field CTO is the Head of Engineering for the Media & Entertainment vertical. You will partner with CTOs and CIOs at Agency Holding Companies, Gaming Studios, and leading generative AI model companies to shape technical roadmaps and help customers become AI-native platforms. You will collaborate with Engineering and Product teams to build M&E primitives and be the strategic technical owner of the vertical — bridging AI infrastructure and inference physics with customer business goals.
Responsibilities
- Own the category-defining architecture for lighthouse deals (e.g., global data consolidation, large-scale infrastructure migrations) and ensure solutions operate at real-world scale, not just POCs.
- Translate infrastructure decisions (data lake locality, storage tiering, etc.) into business and operational impact for executive stakeholders.
- Forensically gather requirements: deconstruct bottlenecks, convert vague business goals into precise engineering requirements (e.g., optimizing inference batch size on L40s to reduce cost-per-token).
- Map customer maturity from service bureau to tech platform and prescribe specific infrastructure interventions.
- Build and validate integration relationships with critical ISVs in the media landscape and define reference architectures for how those tools run best on Nebius infrastructure.
- Chair the M&E Product Summit and use field evidence to prioritize and justify the M&E vertical roadmap.
- Direct an engineering team to produce high-leverage assets such as scalable quick-start reference architectures (example: deploying large models like Llama-405B for use cases such as ad copy creation).
Requirements
- 10+ years of experience in cloud infrastructure, platform engineering, or distributed systems.
- Executive presence with the ability to present layered technical roadmaps to C-suite stakeholders.
- Product-minded: experience defining platform strategy and making principled technical trade-offs (able to decline requests that create technical debt and propose better alternatives).
- High tolerance for ambiguity; ability to proactively build roadmaps and operate in evolving environments.
- Entrepreneurial grit and a forensic mindset: dig into logs, kernels, and P&L to find root causes and truth.
AI Infrastructure & Technical Expertise
- Production-grade expertise across the AI infrastructure stack, including GPU architecture knowledge (H100, L40s) and the physics of model inference and cost-performance trade-offs.
- Kubernetes orchestration (K8s) experience for large-scale workloads.
- High-performance storage and parallel file systems (examples: Lustre, WEKA) and storage tiering strategies.
- Networking constraints and experience with InfiniBand and high-performance Ethernet.
- Inference optimization skills: batching, quantization, KV caching, and latency trade-offs (architecting for both massive throughput and real-time sub-50ms demands).
- Experience with model serving, large language models and multimodal generative models, and practical knowledge of deploying and serving very large models.
Domain Context: Media & Entertainment
- Industry fluency across at least two sub-sectors such as Gaming, Multimodal Generative Models, AdTech, or VFX.
- Ability to prescribe the right operational model (bare-metal vs managed endpoints) for different customers.
Benefits
- 100% company-paid medical, dental, and vision coverage for employees and families.
- 401(k) with up to 4% company match and immediate vesting.
- Parental leave: 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers.
- Remote work reimbursement up to $85/month for mobile and internet.
- Company-paid short-term, long-term disability and life insurance.
- Competitive salary, equity, and opportunities for professional growth.
Compensation
- Competitive salaries ranging from $295k - $365k OTE (On Target Earnings) + Equity, dependent on experience.
Location & Work Policy
- You may work remotely from the United States (SF Bay Area or New York City preferred). Office presence / travel expectations: ~10%–20% for executive meetings and key industry events.
- Flexible working arrangements and remote-friendly policies.