Senior Sales Engineer - Token Factory

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