Quantum Machines (QM) is a global leader in hybrid quantum-classical control systems for quantum computing, a field on the verge of exponential growth. Our innovative hardware and software offer a groundbreaking approach to controlling quantum computers, scaling from individual qubits to arrays of thousands. At the heart of QM is a passionate, ambitious team passionate to advance the world of quantum computing further than it has ever gone before.
We’re looking for a hands-on, technically grounded AI Engineer for Marketing. This role is for a true builder: someone who is creative and understands how systems work under the hood and knows how to come up with concepts and construct them to reliable, scalable, production-grade tools - beyond basic prompt experimentation.
In this role, you will connect complex data infrastructures, GTM systems, and AI implementations. Working on top of infrastructure designed and managed by our core IT and DevOps teams, you will own the application layer lifecycle of AI-powered solutions: from setting up agents and building custom skills to developing full applications, aligning with security policies, and driving continuous iteration.
You will act as a key technical bridge: defining infrastructure and hosting requirements with IT and DevOps, while collaborating cross-functionally with Marketing, Sales, CS, Product, and GTM teams to build internal tools that supercharge efficiency and external applications that drive customer engagement and sales.
What you'll do
Build & Deploy AI Applications, Agents, and Skills: Architect, code, deploy, and maintain custom applications, internal agents, specialized skills, and automated workflows on top of the infrastructure and environments managed by IT and DevOps.
Partner Closely with IT & DevOps: Work side-by-side with IT and DevOps to define technical requirements, ensure alignment with corporate security policies and data governance, and deploy tools seamlessly within the company's established tech stack.
Build Internal & External Tools: Develop customer-facing AI applications to boost engagement and drive revenue, alongside internal software platforms that surface proactive intelligence for cross-functional teams.
Manage Application Data & Integrations: Connect databases, data warehouses (Snowflake), APIs, vector stores, and data streams to power reliable RAG pipelines and ensure seamless data flow across GTM applications.
Cross-Functional GTM Collaboration: Partner with Marketing, Sales, CS, Product, and GTM leads to translate business strategies, buyer intent signals, and operational bottlenecks into high-performing technical solutions.
Ensure Architectural Integrity & Code Quality: Inspect, debug, test, and review both human and AI-generated code to guarantee secure, maintainable, high-performing, and auditable production deployments.
Treat Prompts & Agents as Code: Apply software engineering best practices (versioning, output validation, error handling, monitoring) to prompt management, custom skills, and multi-agent workflows.
Requirements
Technical Experience: 2-4 years of hands-on experience in software development, application engineering, or data engineering with a proven background in building production tools. - Must
Coding & Software Fundamentals: Strong proficiency in Python and/or TypeScript/JavaScript, with solid experience reading, writing, debugging, and auditing code. -Must
Databases, Data Warehouses & APIs: Solid foundation in SQL, database architectures, Snowflake, vector databases (Pinecone, Qdrant, Chroma, etc.), REST APIs, webhooks, and data modeling.-Must
Infrastructure & Security Literacy: Clear understanding of web architecture, API security, and data leakage prevention: enough to articulate requirements to DevOps/IT and build securely within their guidelines. -Must
Fluent English: Excellent written and verbal communication skills.
BSc. in Computer Science, Computer Engineering, or a relevant scientific field. - Must
Nice to have
Technical MarTech experience.
Academic background in Computer Science, Software Engineering, or a related technical discipline.
Experience with GTM systems, CRMs (HubSpot, Salesforce), and automation engines (n8n, Make, Workato).
Hands-on experience building custom web scrapers, RAG pipelines, custom skills, or agentic frameworks (LangChain, LlamaIndex, CrewAI).
Prior experience in a B2B deep-tech, hardware, or SaaS company.