flaschenpost — the fast-growing instant delivery service for beverages and groceries that delivers the weekly shop within 120 minutes, backed by the Oetker-Gruppe, operating in nearly all major metropolitan regions across Germany with over 20,000 colleagues and reinventing an entire industry since 2016 — is seeking an Operations Research & Optimization Engineer (m/w/d) at one of its locations in Münster, Köln, or Berlin. You will join the Operations Research team and co-develop flaschenpost’s Last-Mile-Delivery business — building mathematical optimization models, simulations, and data-driven analyses that directly control the operational logistics workflows of one of Germany’s most exciting and fast-growing e-commerce delivery companies. The role combines mathematical optimization and OR algorithm development in Julia and Python with production system deployment on Kubernetes, Azure, and Databricks. Career changers (Quereinsteiger) with IT enthusiasm and motivation are explicitly welcome.
flaschenpost Top-Angebote — Benefits & Working Culture
Work-Life Balance: Gestalte deinen Arbeitstag flexibel und genieße die Freiheit des mobilen Arbeitens — genuine flexible hours and mobile working, not just on paper
Mobility: Job-Ticket (Deutschland-Ticket) + Business-Bike — so not only working hours are flexible, but also how you get there
Start-Up Energy: Freigetränke · Kicker · Tischtennis — the startup culture perks that make the daily grind enjoyably different from a traditional corporate environment
Learning: Flaschenpost Learning — personalized Learning Journey with diverse and innovative training offerings | Buddy-Programm for structured onboarding support
Technology: Modernste Technik — commitment to technological progress and innovation, with access to the latest tools, platforms, and development environments
Community: Team- und Networking-Events · Betriebssport · Spieleabend · Stammtisch | Corporate Benefits — discounts at online shops and fitness studios
Social: Blutspenden während der Arbeitszeit — social responsibility embedded in the working day
About flaschenpost — Sofortlieferdienst | Part of Oetker-Gruppe | 120-Minute Delivery
Company: flaschenpost — Instant delivery service for beverages and groceries | Founded 2016 | 20,000+ employees | Part of Oetker-Gruppe since 2020
Mission: Deliver the weekly grocery shop within 120 minutes — operating in nearly all major metropolitan regions of Germany, reinventing an entire industry from first principles
OR Team Role: The Operations Research team develops the mathematical optimization models, simulations, and data-driven analyses that control flaschenpost’s operational logistics workflows — from last-mile route optimization through to delivery scheduling, capacity planning, and operational efficiency improvement
Locations: Münster (HQ) · Köln · Berlin — all three locations are valid for this OR Engineering role
Quereinsteiger: Career changers with IT enthusiasm and motivation are explicitly welcome — flaschenpost’s priority is finding people who recognize themselves in the role requirements and are motivated to grow into them
Why This flaschenpost Operations Research Role Is a Career-Defining Opportunity in 2026
Real-World OR at Scale: Developing mathematical optimization models that directly control the last-mile delivery operations of a 20,000-employee instant delivery company operating in every major German metropolitan region is fundamentally different from academic OR — the algorithms you write run in production, optimize tens of thousands of real deliveries, and directly determine whether customers receive their groceries within the promised 120-minute window
Julia + Python in Production: Working with both Julia and Python for production OR engineering — Julia’s exceptional numerical computing performance for optimization solvers, combined with Python’s ecosystem breadth for data processing and ML integration — provides the rare and genuinely valuable multi-language OR engineering experience that only a handful of companies in the world can offer
Modern Cloud & Data Platform Stack: Production deployment on Kubernetes, Azure, and Databricks — the specific combination of container orchestration, cloud platform, and lakehouse data platform that represents the current state of the art for production-scale OR and analytics engineering
Oetker-Gruppe Backing: flaschenpost’s position within the Oetker-Gruppe provides the financial stability, corporate resources, and long-term strategic backing that pure startups lack, while maintaining the fast-paced, innovation-driven startup culture that makes the work genuinely exciting
Position Overview — OR & Optimization Engineer at flaschenpost
This Operations Research & Optimization Engineer (m/w/d) role at flaschenpost is a full-time, technically demanding OR engineering position within the company’s Operations Research team, responsible for developing and operating the mathematical optimization models, simulations, and data-driven analyses that control flaschenpost’s Last-Mile-Delivery logistics in the IT world of flaschenpost. You will — together with the team — take responsibility for providing mathematical optimization models, simulations, and data-driven analyses for last-mile delivery; develop and operate production systems that control the operational logistics workflows of flaschenpost; implement mathematical optimization approaches in Julia and Python; take responsibility for the deployment and operation of solutions on modern cloud and data platforms including Kubernetes, Azure, and Databricks; and through close collaboration with product, IT, and specialist departments, translate analytical models into real business processes. The role requires a degree in (Wirtschafts-)Informatik or a comparable MINT discipline, the ability to implement algorithmic optimization and analysis approaches in Python or Julia, experience in mathematical optimization design and development, meta-heuristics, discrete-event simulations or comparable applications, and the ability to communicate technical topics clearly in German and English.
Why This flaschenpost Operations Research Engineer Role Is the OR & Optimization Career of 2026 in Germany: OR and optimization engineers with the ability to implement mathematical optimization models and meta-heuristics in Python or Julia, experience designing discrete-event simulations or comparable OR applications, interest in deploying production optimization systems on Kubernetes, Azure, and Databricks, and the communication skills to translate complex analytical models into business processes — have an exceptional opportunity at flaschenpost to work on last-mile delivery optimization problems at a scale and operational consequence that most academic and consulting OR positions cannot match, within a fast-paced, technology-first environment backed by the Oetker-Gruppe and offering genuine flexible working, strong learning investment, and the career-defining experience of building production OR systems at a company that is reinventing grocery delivery across Germany.
Key Responsibilities — Dein Liefergebiet
Mathematical Optimization Models & Data-Driven Analytics for Last-Mile Delivery
- Take responsibility — together with the team — for providing mathematical optimization models, simulations, and data-driven analyses for last-mile delivery within flaschenpost’s IT ecosystem — developing the specific combinatorial optimization models, routing algorithms, assignment heuristics, and operational analytics that determine how flaschenpost allocates deliveries to drivers, sequences delivery routes, manages time window constraints, adapts to real-time demand fluctuations, and continuously improves the operational efficiency that makes 120-minute grocery delivery both commercially viable and logistically reliable at the scale of Germany’s major metropolitan regions
- Develop and operate production systems that control the operational logistics workflows of flaschenpost — taking the full engineering ownership of OR solutions from algorithmic concept through mathematical model formulation, algorithm implementation in Julia and Python, testing and validation, production deployment on Kubernetes, and ongoing operational monitoring and improvement, ensuring that the production OR systems that run flaschenpost’s daily delivery operations are reliable, performant, correctly optimizing for the right objectives, and maintainable by the wider OR engineering team
- Translate analytical models into real business processes through close collaboration with product, IT, and specialist departments — applying the cross-functional communication and translation skills that bridge the gap between mathematical optimization model formality and the operational business processes of a 120-minute delivery company, working with product managers who understand customer experience requirements, IT engineers who build the technical systems the OR algorithms integrate with, and operations specialists who understand the practical constraints and realities of last-mile delivery that mathematical models must respect to be useful in production
Julia & Python Implementation — Mathematical Optimization & Meta-Heuristics
- Implement mathematical optimization approaches in Julia and Python — applying Julia’s exceptional numerical computing performance (for solver-intensive optimization problems involving large-scale integer programming, mixed-integer linear programming, or constraint programming formulations where Julia’s computational speed advantage over Python is significant) and Python’s rich ecosystem (for data processing, ML integration, API development, and the broader software engineering context that production OR systems require) to build the specific optimization algorithms that flaschenpost’s last-mile delivery challenges demand
- Develop and apply mathematical optimization methods — including exact optimization approaches (branch-and-bound, branch-and-price, column generation), meta-heuristics (simulated annealing, tabu search, genetic algorithms, large neighborhood search, ant colony optimization), local search methods, and constructive heuristics — selecting the approach that best balances solution quality, computational time, and implementation complexity for each specific last-mile delivery optimization problem, applying the theoretical OR foundation and practical algorithm engineering judgment that distinguishes engineers who build OR solutions that work in production from those who only solve textbook instances
- Design and develop Discrete-Event Simulations (DES) for last-mile delivery system analysis — building the discrete-event simulation models that allow flaschenpost’s OR team to evaluate the performance implications of changes to delivery zone configurations, fleet sizing, order batching policies, time window structures, and other operational parameters before implementing them in the live delivery system, applying simulation output analysis methodology (statistical testing, variance reduction techniques, warm-up period identification) that produces statistically valid performance estimates rather than potentially misleading single-run simulation results
Cloud Deployment — Kubernetes, Azure & Databricks Production Systems
- Take responsibility for the deployment and operation of optimization solutions on Kubernetes, Azure, and Databricks — designing and implementing the containerized microservice deployments, Kubernetes cluster configurations, Azure cloud infrastructure, and Databricks data pipeline integrations that take Julia and Python OR algorithms from development environment through to production-grade, scalable, monitored, and maintainable deployment, applying the DevOps and MLOps engineering practices (CI/CD pipelines, automated testing, deployment automation, performance monitoring, alerting) that ensure production OR systems operate reliably within the operational constraints of a real-time delivery business
- Operate and maintain production OR systems in flaschenpost’s cloud environment — monitoring system performance, diagnosing and resolving production incidents, implementing improvements to system reliability and computational efficiency, and managing the ongoing engineering maintenance that keeps production optimization systems performing correctly as the business scales, operational patterns evolve, and new optimization requirements emerge from the product and operations teams
Requirements Summary
- Completed degree in (Wirtschafts-)Informatik or a comparable MINT discipline — providing the algorithmic, mathematical, and software engineering foundations for OR model development and production system engineering at flaschenpost’s technical level
- Ability to implement algorithmic optimization and analysis approaches securely in Python or Julia — demonstrated through relevant coursework projects, research work, personal projects, or professional experience in algorithmic implementation
- Experience or strong motivation to develop experience in mathematical optimization design, meta-heuristics, discrete-event simulations, or comparable OR applications — the specific technical OR knowledge that the role’s core responsibilities require
- Clear communication of technical topics in German and English — for effective collaboration with product, IT, and operations colleagues across flaschenpost’s organization
- Enthusiasm for translating analytical problem statements into innovative solutions — the intellectual curiosity and practical engineering drive that makes an OR engineer genuinely productive rather than theoretically interested
- Quereinsteiger explicitly welcome — flaschenpost values IT enthusiasm and motivation alongside formal qualifications and is open to candidates transitioning from adjacent technical fields
About flaschenpost & Operations Research Careers in Germany 2026
flaschenpost‘s ambition — to deliver the weekly grocery shop within 120 minutes, to nearly every major metropolitan area in Germany, reliably and at scale — is only achievable because of the quality of the mathematical optimization that runs underneath the visible product. Every route that a delivery driver takes, every sequence in which orders are batched, every assignment of customer delivery windows to available capacity, every adaptation to real-time demand fluctuations — all of these are the direct output of the Operations Research models and algorithms that the OR engineering team builds, deploys, and continuously improves. For OR engineers who want to work on problems that are simultaneously mathematically rich (the Vehicle Routing Problem with Time Windows and all its operational variants is one of the most extensively studied and genuinely hard combinatorial optimization problems in the OR literature), operationally consequential (a better algorithm means faster deliveries, less waste, more efficient routes, and a better customer experience for hundreds of thousands of daily deliveries), and professionally stimulating (using Julia for high-performance optimization in production alongside Python’s ecosystem breadth in a Kubernetes/Azure/Databricks cloud environment) — flaschenpost’s Operations Research team in 2026 is one of the most genuinely exciting and career-advancing OR engineering environments available in Germany.
Your Career Growth Path: OR & Optimization Engineer → Senior OR Engineer → Lead OR Engineer → Head of Operations Research → Director of Optimization & Analytics — a mathematically rigorous, technically frontier, and commercially consequential Operations Research engineering career trajectory built at the center of one of Germany’s most ambitious and fastest-growing instant delivery companies.
Who Should Apply?
- OR/Optimization Engineers — Python/Julia (Germany Based): With mathematical optimization, meta-heuristics, or discrete-event simulation experience in Python and interest in developing Julia skills who want a production OR engineering role at a 20,000-employee instant delivery company backed by the Oetker-Gruppe
- Data Scientists — OR Transition: With strong Python data science backgrounds and exposure to optimization or simulation who want to specialize in production-grade Operations Research engineering within flaschenpost’s fast-paced logistics optimization context
- Software Engineers — Algorithm Focus: With strong algorithmic problem-solving backgrounds and Python engineering capability who want to move into the OR and mathematical optimization domain within a technically demanding, production-at-scale engineering environment
- Academic OR Researchers — Industry Transition: With OR, combinatorial optimization, or simulation research backgrounds who want to apply their theoretical knowledge to real operational problems at a production scale that academic research environments cannot provide
- Quereinsteiger — IT Enthusiasm + MINT Background: From adjacent technical fields (industrial engineering, applied mathematics, physics, or computer science) who recognize themselves in the role’s core requirements and want the structured development opportunity that flaschenpost’s Buddy Programme and Learning offerings provide
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