About Company
Playbook is building the Operating Memory for regulated industries.
In life sciences and other highly regulated sectors, companies run on thousands of written procedures — SOPs, work instructions, policies, manuals, and how-to guides. Employees are expected to find, understand, follow, and stay trained on the right procedure at the right moment. That system is breaking under the weight of information overload.
Playbook turns procedural documents into structured, usable data — making critical operational knowledge consumable by humans, software systems, and AI agents. This living layer of procedural knowledge is what we call Operating Memory.
We work with leading global life sciences companies and are building the foundational data model for procedural knowledge in regulated industries. We're an early-stage, founder-led company with high ambition, high standards, and a strong bias toward execution.
Why now
Enterprise AI agents can't run on fragile data pipelines. They need deterministic, auditable, and low-latency infrastructure under the hood.
The Role
Our core product relies on a highly sophisticated data layer. Our research team has successfully built a proprietary domain-specific ontology and a multi-layer procedural hypergraph that extracts complex regulatory claims from text.
We're hiring our Staff Software Engineer to build the production engine that scales these foundational models into a rock-solid product. You don't need to be an AI theorist or machine learning researcher. Your job is to understand what our research pipelines output on a functional level, and serve as the engineering anchor who scales our architectures into reliable, low-latency, and cost-effective enterprise infrastructure.
You will report directly to our CTO and operate as a senior Individual Contributor, collaborating closely with our ontology research team and our core backend engineers. Your KPIs are concrete: pipeline inference latency and throughput optimization, infrastructure cost-efficiency, and system uptime/reliability under enterprise load.
What You'll Own
Productionize complex graph models — take our proprietary extraction logic and multi-layer procedural hypergraphs from the research layer and deploy them into robust production environments.
Build scalable architecture — design and write production-grade, modular backend systems and APIs to expose Playbook's Operating Memory to enterprise client environments (e.g., Microsoft Copilot Studio, SAP Joule, Veeva).
Cloud-native infrastructure — design, scale, and maintain our cloud-native infrastructure on AWS using Kubernetes, ensuring bank-grade stability and auditable version control.
Optimize system performance — profile and optimize data pipelines to meet strict enterprise SLAs regarding latency, throughput, and cloud spend.
Own CI/CD & reliability — build and maintain automated testing and continuous integration/deployment pipelines optimized for rapid, iterative code shipping.
Anchor engineering decisions — serve as the senior IC who shapes how research output becomes production reality, working shoulder-to-shoulder with researchers and backend engineers.
Life sciences manufacturing: we translate regulated procedures into machine-executable logic that governs behavior across people, systems, and AI.
What You Bring
7+ years of professional software engineering experience with a track record of shipping production backend systems, preferably within early-stage or venture-backed startups.
Mastery of Python combined with professional proficiency in at least one other language (C#, TypeScript, or Java).
Advanced academic degree (Master's, PhD, or German Diplom) in Computer Science, Physics, Mathematics, or a highly quantitative engineering field.
Infrastructure mastery — hands-on experience with cloud infrastructure (AWS) and containerization/orchestration at scale (Docker, Kubernetes).
Data & model pipelines — proven track record of deploying and serving complex data models, large-scale graph structures, or heavy computational payloads in live, user-facing production environments.
Senior systems judgment — the ability to make sound architectural trade-offs under ambiguity, not just write clean code.
Nice to have
Experience working alongside research, data science, or ontology teams.
Familiarity with high-performance data serving frameworks or infrastructure tools (e.g., Ray, Triton Inference Server, vLLM).
Practical experience working with graph databases or automated knowledge engineering tools.
Probably Not a Fit
This role likely isn't for you if you…
Prefer a narrow scope and well-defined tasks handed down from a larger platform team.
Need heavy structure, many layers of sign-off, or mature internal tooling to ship.
Want to focus on AI/ML research rather than production systems engineering.
Are looking for a relaxed, stable role in an established organization rather than a high-performance early-stage environment.
Why Playbook®
Define the production backbone of a new category at the intersection of enterprise software, regulated operations, and AI infrastructure.
Foundation
This role has real influence on the foundation of the company. You will shape the architecture that every future product capability is built on.
Build from Zero
Real ownership from day one. High ambition, high standards, and a strong bias towards execution - with the runway to ship.
An emerging category
Work at the intersection of AI, compliance, and enterprise operations — genuinely new ground.
State-of-the-art hardware
A hardware setup of your choice, designed for deep, focused engineering work.
Remote-first, high-trust
A culture where execution and impact are what matter — not hours or optics.
Think this is your
kind of challenge?
Send us your CV, a short note on why you'd be a great fit, and links to anything you've built. We're looking forward to hearing from you.
