Senior Sales Engineer

The role 

We are building a high-performance AI inference platform for developer-native teams running latency- and cost-sensitive workloads at scale.

In AI infrastructure, PoC success does not guarantee production success. This role ensures that what we commit to is scalable, efficient, and aligned with platform strategy.

We are looking for a Senior Sales Engineer to become a foundational technical partner to our customers and a force multiplier for Sales and Engineering. You will shape complex AI workloads from first discovery through production feasibility validation, ensuring technical rigor, economic viability, and scalable architecture decisions.

You will operate at the intersection of customer ambition, engineering reality, and commercial growth, influencing:

• Revenue quality
• Engineering focus
• Product evolution
• Customer trust at scale

You’re welcome to work remotely from Europe.

Your responsibilities will include: 

Strategic Technical Discovery

• Lead deep technical discovery with engineering teams and technical founders
• Understand model requirements, traffic expectations, latency constraints, GPU economics, and system dependencies
• Translate customer ambition into production-feasible architectures
• Identify hidden technical risks early

Commercial Acceleration

• Partner tightly with Sales on strategic deals
• Influence deal strategy through architectural clarity
• Prevent misaligned commitments before engineering allocation
• Increase PoC-to-production conversion by ensuring technical realism

PoC Architecture & Validation

• Define measurable success criteria (latency, TTFT, throughput, cost envelope)
• Classify workload complexity and required optimization depth
• Align appropriate resources (ML Solution Architects, engineering, GPU capacity, etc.)
• Drive structured Go / No-Go decisions
• Prevent uncontrolled customization or hidden R&D

Pattern Recognition & Platform Leverage

• Identify recurring configuration patterns across customers
• Quantify demand for advanced optimizations (quantization, speculative decoding, etc.)
• Surface structured insights to Product and Engineering
• Help evolve platform capabilities based on real workload data

We expect you to have: 

• Deep understanding of AI inference systems and GPU-backed infrastructure
• Experience with LLM workloads and performance-sensitive environments
• Experience with inference frameworks and libraries (e.g., vLLM, SGLang, TensorRT-LLM).
• Ability to reason about latency, throughput, cost, and architecture tradeoffs
• Strong customer presence with engineering-first organizations
• Comfort challenging assumptions and pushing back constructively
• Commercial awareness – you understand that engineering time is a strategic resource

Preferred technical stack: 

• Programming Languages– Python
• Frameworks and Libraries– vLLM, SGLang, TensorRT-LLM, OpenAI/Anthropic SDKs
• Frameworks for Agentic Pipelines : Langchain / Langsmith / smolagents / equivalent
• API and Web Frameworks– FastAPI, Flask
• MLOps and DevOps tools– Kubernetes (K8s), Docker, Git
• Cloud Platforms– AWS (SageMaker, Bedrock), GCP (Vertex AI), Azure (Azure ML)

What success looks like: 

• Strategic deals are technically sound before engineering engagement
• PoCs are clearly scoped and economically justified
• Engineering capacity is allocated predictably
• Conversion to production improves
• Customers view you as a trusted architectural advisor