Jobs at InPost

Own complex, cross-team technology programs from planning through launch. Build roadmaps, manage dependencies and risks, define KPIs, coordinate security and operational requirements, lead governance and readiness reviews, communicate with technical and executive stakeholders, mentor associate TPMs, and use AI tools to automate program management.

Administer and configure CRM platforms, including pipelines, custom objects, permissions, workflows, integrations, and automations. Translate parcel-machine deployment and partner onboarding processes into scalable CRM logic. Maintain data quality through audits, deduplication, validation, and lifecycle management. Build dashboards and reports for international stakeholders, gather cross-market requirements, and create documentation, training materials, and user support processes.

Own the technical roadmap and evolution of InPost’s cloud-based data platform across Azure and Databricks. Drive scalability, reliability, security, operational excellence, FinOps, cost optimization, DevOps/DataOps, CI/CD, and Infrastructure as Code practices. Manage technology vendors, procurement, contracts, SLAs, licensing, budgets, purchase orders, and invoices. Partner with Data, AI, Engineering, Finance, and business stakeholders across multiple markets.

Staff Software Engineer responsible for quality and reliability strategy across backend APIs and native iOS and Android applications. The role includes designing testable architectures, quality gates, release pipelines, device and OS coverage, flakiness controls, cross-team incident resolution, technical leadership, mentoring, quality metrics, and AI governance. The engineer will influence multiple teams, contribute hands-on code and tooling, and promote shift-left quality ownership across the engineering organization.

Lead and develop the engineering team, own the InPost Pay product roadmap, and provide technical direction across architecture, coding standards, security, quality, scalability, and performance. Collaborate with product teams and stakeholders to align priorities and deliver reliably. The role requires strong Java expertise, cloud infrastructure and migration experience, people leadership, delivery management, product ownership, and responsible use of AI tools.

Design, develop, maintain, and deploy Java backend applications and microservices. Build scalable features, improve performance, security, and reliability, troubleshoot issues, participate in code reviews, document systems, and collaborate with cross-functional teams. The role also involves supporting continuous improvement and knowledge sharing in Agile/Scrum environments.

Owns end-to-end recruitment for Tech, Data, and AI roles. Partners with hiring managers on role definition, hiring strategy, market insights, interviews, and final decisions. Sources candidates through LinkedIn and Boolean search, manages candidate experience, builds talent pools, uses recruitment metrics to optimize processes, and supports employer branding initiatives.

Analyze large-scale, real-time data across marketing, product, customer, sales, and logistics domains. Gather stakeholder requirements, develop scalable data models and analytics solutions, process data using Python/PySpark and SQL, and create actionable insights and recommendations. Build and maintain high-quality code, support data-driven decisions, coordinate visualizations, and collaborate with data engineers, data scientists, business analysts, and business stakeholders.

Lead a 10–15-person multidisciplinary AI engineering team and own the Group-wide AI Engineering strategy. Build AI-native products and backend platforms, establish development standards including CI/CD with evaluations and test sets, guide multi-agent and streaming workflows, improve architecture and engineering processes, and partner with product, data, vendors, and technology stakeholders. Responsibilities also include hiring, team development, budgeting, and influencing AI product direction across the organization.

Lead and develop an engineering team while driving greenfield initiatives and large-scale software projects from concept through production. Provide technical leadership in Java, architecture, system design, and modern engineering practices. Partner with Product Managers and stakeholders, manage delivery, communicate risks, support organizational change, and improve development processes. The role requires strong people leadership, cloud-native experience in GCP or AWS, product ownership, AI tool proficiency, and effective communication.

Own and advance the evaluations platform for Von Halsky, InPost’s conversational AI shopping assistant. Lead LLM-judge pipelines, evaluation datasets, Polish-language golden sets, root-cause analysis, anomaly detection, and eval-driven development. Define measurable quality standards with product and business stakeholders, set technical direction, review code, mentor engineers, and ensure releases are reproducibly evaluated and gated.

Design and deliver scalable Microsoft Dynamics 365 and Power Platform solutions across international teams and markets. Lead architecture, integrations with enterprise systems and Azure services, platform upgrades, migrations, and environment modernization. Contribute to technical strategy, architecture reviews, engineering quality, AI-enabled workflows, and modern DevOps practices. Mentor engineers, support knowledge sharing, and take ownership of complex technology initiatives.

Lead technology due diligence, post-acquisition integration, and technology preparation for new market launches. Coordinate teams, systems, processes, budgets, milestones, dependencies, and stakeholder alignment across international markets without direct authority. Assess technology gaps, drive adoption of group standards, manage external partners, and develop integration playbooks, templates, reporting, and knowledge resources.

Own and develop InPost Pay fast-checkout products, including checkout and BNPL solutions. Translate customer needs into product requirements and user stories, manage tactical and strategic backlogs, and lead initiatives from concept through launch. Collaborate with technology, UX, data, marketing, stakeholders, and vendors; monitor market trends and competitors; use analytics, A/B testing, and research to guide decisions; and mentor team members.

Design and deploy data science and machine learning solutions for large-scale parcel delivery operations. Responsibilities include routing and allocation algorithms, vehicle and route optimization, graph-based analysis, clustering, regression, forecasting, and production model deployment. The role translates logistics and business problems into scalable data solutions that improve delivery speed, cost efficiency, and customer experience.

Design, administer and improve enterprise WAN/SD‑WAN, LAN and WLAN infrastructure; monitor performance; deploy network projects and equipment; troubleshoot LAN/WAN issues; automate operations; maintain documentation; and provide technical support to cross‑functional teams.

Manage and maintain WAN/SD-WAN, LAN and WLAN infrastructure; monitor performance and capacity; implement network security and VPN solutions; install and configure network devices; automate tasks with scripting and AI support; produce standards and documentation; provide technical support and travel to support projects.

As an AI Product Manager, you will identify AI-driven solutions, manage product roadmaps, develop Go-To-Market strategies, and drive adoption of AI products across InPost Group.

Design and implement high-performance Java/Spring Boot microservices, manage event-driven integrations with Kafka, own data schema and Postgres performance, operate containerized systems on Docker/Kubernetes/GCP, ensure code quality and CI/CD, mentor engineers, deliver features end-to-end and produce technical documentation.

Lead and scale a ~24-25 engineer payments and checkout organization across three teams. Develop engineering managers and tech leads, set technical and delivery standards, drive architecture and cloud strategies, remove bottlenecks, implement AI-enabled engineering practices, define domain KPIs/OKRs/SLAs, and represent engineering in cross-functional initiatives to ensure reliable, measurable delivery at scale.