What You'll Do

• Source and manage relationships with equity investors (VC, growth, sovereign wealth), debt providers (banks, infra funds, asset-backed lenders), and strategic partners
• Build and maintain detailed financial models for fundraising: DCFs, LBO-style returns analyses, project finance models for GPU infrastructure
• Structure and negotiate term sheets, credit facilities, and co-investment arrangements
• Own the data room, investor materials, and due diligence process for all capital raises
• Monitor capital markets for relevant financing opportunities, benchmark Lyceum's capital structure against peers, and advise on optimal timing and instruments
• Support grant and subsidy applications (e.g. IPCEI, EU Innovation Fund) where relevant

• 2-4 years in investment banking (coverage or capital markets), infrastructure/project finance, private credit, or growth equity
• Strong financial modelling and structuring skills across equity and debt instruments
• Track record of investor or lender relationship management
• Understanding of infrastructure economics, asset-backed financing, or technology capital markets
• Fluent in English; German is a plus
• High conviction about the opportunity in European AI infrastructure

What You’ll Do

• Support fundraising processes end-to-end: data rooms, investor materials, financial models, due diligence coordination

• Own recurring FP&A: monthly reporting, budget tracking, variance analysis, and cash flow forecasting

• Build and maintain financial models for unit economics, scenario planning, and strategic decisions (build vs. lease, pricing, capacity expansion)

• Prepare investor updates, board decks, and KPI dashboards

• Handle ad-hoc finance and strategy projects: grant applications, partnership economics, contract analysis

• Work with external accountants, auditors, and legal counsel on compliance, tax, and corporate housekeeping

• 1-3 years in investment banking, private equity, venture capital, corporate finance, or a high-growth startup finance team

• Excellent financial modelling skills (Excel/Sheets)

• Strong attention to detail and ability to synthesise complex information into clear narratives

• Comfortable operating in ambiguity. You’ll often define the problem before solving it

• Fluent in English; German is a strong plus

• Genuine interest in infrastructure, cloud, or AI markets

You will enable the expansion of Lyceum’s bare-metal and managed service GPU offering, taking our EU-sovereign cloud offering to the next step. This exciting role includes both a technical customer-facing function and the opportunity to develop and contribute to a novel software stack – changing the way GPUs are being used today.

Your focus:

This role has both a customer and a developer component. On the customer side, you’ll be responsible for technical customer requests, both during the onboarding process and later in the lifecycle. On the developer side, you’ll help to build out and improve our managed services such as Slurm or Kubernetes.

Your KPIs:

• Response time / error resolving
• Customer satisfaction
• Develop and improve software offering on top of bare metal offering

We consider candidates from diverse backgrounds, with a deep love for technical challenges and the desire to take on ownership beyond what’s reasonably expected.

What we’re looking for:

• 3+ years of experience working with GPU infrastructure (bare metal, VM, cloud)
• Experience with Kubernetes and/or Slurm cluster deployment and management
• Hands-on experience with the NVIDIA software stack (drivers, CUDA, DCGM, GPU)
• Operator Familiarity with high-performance networking (InfiniBand, NVLink, RoCE) 
• Strong Linux systems administration skills 
• Solid networking fundamentals (IP addressing, firewalls, VPNs, load balancing) 
• Familiarity with IaC tools (Ansible, Terraform, or similar) 
• Strong written and verbal communication in English

Nice to have:

• Experience with confidential computing technologies (Intel TDX, AMD SEV-SNP)
• Experience with NVIDIA DGX/HGX systems or multi-node GPU clusters
• Knowledge of MIG (Multi-Instance GPU) partitioning and GPU virtualisation
• Background in HPC or AI/ML workloads
•  Experience in a customer-facing technical role – you can explain complex infrastructure clearly and handle urgent requests professionally

• Relentless: You can't leave something broken. You don't stop because it got hard or because no one is watching.
• Set The Bar: You're harder on yourself than anyone else would be. You don't need external accountability to care about quality.
• Shape The Game: You don't wait for a playbook and you don't need one. You walk into genuinely new territory and figure it out. You move before anyone asked you to.
• Systems First: You don't solve problems by adding people or effort. You build systems that make the problem smaller. Your first instinct is to automate, not to handle it manually.
• Technical Curiosity: You want to understand the product you're selling. You're not intimidated by GPU clusters, Kubernetes, or inference latency, you're excited by it.

• 1–3 years consulting banking oder einem startup 
• Broad understanding of the GTM tooling landscape and best practices (CRM, outbound tools, automation)
• Based in Berlin or willing to relocate
• Fluent in English; German is a strong plus
• Plus, but not required: Strong technical literacy, ideally a CS degree or hands-on engineering experience, so you can engage credibly with our product and infrastructure teams without needing everything translated.

• Outstanding team: Work with some of the best engineers in the world, coming from hedge funds, big tech, AI startups, and top universities
• Once in a lifetime opportunity: Early-stage company in the fastest-growing market in the world
• Ownership: Shape how European AI companies access GPU compute
• European mission: Build sovereign, GDPR-compliant AI infrastructure for the next generation of deep-tech
• Growth trajectory: This role evolves into a GTM leadership position as Lyceum scales or gives you the playbook to start your own company

What You’ll Do

• Own end-to-end procurement of GPU servers, networking equipment, and data centre capacity across European locations

• Build and maintain relationships with key suppliers: NVIDIA, OEMs (Supermicro, Dell, Lenovo), data centre operators, and channel partners

• Negotiate pricing, lead times, warranties, and SLAs for multi-million euro hardware purchases

• Manage supply chain logistics: ordering, customs, delivery coordination, rack-and-stack, and commissioning

• Develop and maintain a hardware procurement roadmap aligned with Lyceum’s capacity planning and customer demand

• Conduct due diligence on secondary-market GPU inventory and evaluate build-vs-buy-vs-lease decisions

• Track market pricing, availability trends, and upcoming hardware releases to inform strategic purchasing decisions

• 2-4 years in hardware procurement, supply chain management, or technical sourcing - ideally in data centres, cloud, HPC, or semiconductor sectors

• Demonstrated track record negotiating high-value hardware or infrastructure contracts

• Strong understanding of GPU server architectures, networking (InfiniBand, RoCE), and data centre power/cooling requirements

• Comfortable working across time zones with global suppliers and managing complex logistics

• Highly detail-oriented with strong project management skills

• Fluent in English; German is a plus

Your focus

-              Team leadership: Run the engineering team's day-to-day: sprint planning, prioritisation, code review culture, and hiring as we scale.

-              Architecture ownership: Drive technical architecture decisions across our platform, balancing speed of delivery with long-term scalability.

-              Product development: Be a key voice in product direction alongside the CTO, contributing both strategic thinking and hands-on implementation.

- Engineering excellence: Set the bar for engineering quality, observability, testing, and deployment practices.

Your KPIs

-              Shipping velocity and consistency of the engineering team

-              Feature release timelines vs. roadmap commitments

-              Adoption of AI-assisted development workflows across the team

Requirements

-              7+ years of experience in high-impact technical environments (startups, big tech, or research labs)

-              Background in GPU infrastructure, HPC, or compute orchestration

-              Deep proficiency in Go and Python

-              Excellent communicator who can align engineering work with business priorities

-              Track record of shipping complex products in fast-moving environments

-              Experience owning technical architecture decisions end-to-end

-              Comfortable operating with high autonomy in an early-stage setting

Nice to have

-              Experience with large-scale distributed AI inference systems

-              Knowledge of NVIDIA Dynamo

-              Experience with Rust

-              Once in a lifetime opportunity: Early-stage company in the fastest-growing market in the world

-              Ownership: Shape how European AI companies access GPU compute

Your focus

-              Platform development: Build and improve core platform services in Go and Python: the systems that allocate, schedule, and manage GPU resources for our customers.

-              Performance and reliability: Profile, benchmark, and optimise critical paths. Investigate and fix issues in production.

Your KPIs

-              Code shipped and components owned

-              Time-to-resolution on assigned issues

-              Ramp speed and expanding scope of ownership over time

Requirements

-              0-2 years of work experience in a technical environment (internships count)

-              Strong proficiency in Go and/or Python

-              Comfortable working in Linux environments

-              Genuine interest in infrastructure, distributed systems, or performance engineering

-              High agency

Nice to have

-              PhD or publications in large-scale systems, distributed computing, or related fields

-              Competition track record — olympiads, ICPC, Kaggle, top-100 in anything

-              Contributions to open-source infrastructure projects

-              Experience with Kubernetes, container runtimes, or GPU tooling

-              Familiarity with networking, storage, or virtualisation at a systems level

-              Once in a lifetime opportunity: Early-stage company in the fastest-growing market in the world

-              Ownership: Shape how European AI companies access GPU compute

Your focus

-              Scalability: Architect and implement the systems that allow our inference platform to scale to thousands of concurrent users. This includes request routing, load balancing, autoscaling, and resource scheduling across GPU clusters.

-              Reliability and observability: Build robust monitoring, alerting, and incident response tooling. Design for graceful degradation, automatic recovery, and minimal downtime.

-              Performance engineering: Profile and optimise the full inference path from request ingestion through model execution to response delivery. Identify and eliminate bottlenecks at every layer.

-              Infrastructure evolution: Evaluate and integrate open-source inference frameworks and tooling (Dynamo, vLLM, Triton, etc.) where they improve throughput, latency, or stability of the serving stack.

Your KPIs

-              Platform uptime and availability (SLA adherence)

-              P50/P95/P99 latency and throughput under load

-              Time-to-detection and time-to-resolution for incidents

-              Scalability milestones (concurrent users, requests per second, GPU utilisation)

Requirements

-              3+ years of experience in backend, infrastructure, or systems engineering

-              Strong proficiency in Go and Python

-              Experience building or operating a model serving platform or ML platform

-              Solid understanding of systems performance - profiling, benchmarking, and optimising latency and throughput

-              Familiarity with observability tooling (Prometheus, Grafana, OpenTelemetry, or similar)

-              Understanding of security fundamentals - network isolation, authentication, encryption, multi-tenancy

Nice to have

-              Experience with NVIDIA Dynamo or similar inference orchestration/routing frameworks

-              Hands-on experience with GPU serving infrastructure (vLLM, Triton, TensorRT-LLM)

-              Experience with Kubernetes in a production environment (deployment, networking, resource management)

-              Experience operating at scale (10k+ RPS, multi-region, multi-cluster)

-              Comfortable working on-call or in incident response when things break

-              Once in a lifetime opportunity: Early-stage company in the fastest-growing market in the world

-              Ownership: Shape how European AI companies access GPU compute

-              European mission: Build sovereign, GDPR-compliant AI infrastructure for the next generation of deep-tech

Your focus

-              Feature development: Design and ship new platform capabilities - from supporting new model architectures and serving frameworks to building out API features that customers are asking for.

-              Customer-facing engineering: Work closely with customers and the commercial team to understand real-world usage patterns, translate feature requests into technical designs, and iterate based on feedback.

-              Developer experience: Improve the end-to-end experience of deploying and running inference on Lyceum, from initial setup through to monitoring and debugging in production.

Your KPIs

-              Number of platform features shipped

-              Time from customer request to feature availability

-              Breadth of supported models, frameworks, and deployment configurations

Requirements

-              3+ years of experience in software engineering, with a focus on backend or infrastructure systems

-              Strong proficiency in Go and Python

-              Hands-on experience with at least one ML inference serving framework (vLLM, TGI, etc)

-              Solid understanding of how large language models and other AI models are deployed and served in production

-              Experience working with REST/gRPC APIs and designing developer-facing interfaces

Nice to have

-              Familiarity with GPU scheduling, batching strategies, or inference optimisation (quantisation, speculative decoding, etc.)

-              Experience with Kubernetes and container orchestration in a production setting

-              Knowledge of AI model formats and conversion pipelines (GGUF, SafeTensors, ONNX)

-              Background in developer tools, platform engineering, or API design

-              Once in a lifetime opportunity: Early-stage company in the fastest-growing market in the world

-              Ownership: Shape how European AI companies access GPU compute

-              European mission: Build sovereign, GDPR-compliant AI infrastructure for the next generation of deep-tech

What You’ll Do

• Execute and iterate on outbound campaigns: prospecting, sequencing, personalisation, and follow-up
• Support inbound lead qualification, respond to inbound interest, and manage early-stage pipeline
• Research and map target accounts, ICPs, and market segments
• Analyse GTM metrics (conversion rates, pipeline velocity, CAC) and surface insights to the team
• Experiment with new channels: community, content, partnerships, events, and developer advocacy
• Assist with marketing content: case studies, comparison pages, social posts, and sales collateral

Lyceum is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees and interns.

What You'll Do

• Conduct user interviews and synthesise customer feedback into actionable product insights
• Define and prioritise feature requirements in close collaboration with engineering leads
• Write specs, user stories, and acceptance criteria for new features
• Coordinate cross-functionally between engineering, GTM, and support to ensure smooth feature launches
• Analyse product usage data to identify friction points and opportunities for improvement
• Contribute directly to product decisions - your input shapes what gets built next

• Currently pursuing or recently completed a degree in Computer Science, Engineering, Business, or a related field
• Genuine curiosity about developer tools, cloud infrastructure, or AI/ML
• Strong communication skills: you can talk to an ML engineer and a sales lead in the same afternoon
• Analytical mindset: comfortable with data, metrics, and structured thinking
• Bias toward action: you ship, not just plan
• Prior internship or project experience in product, engineering, or a startup environment is a plus