What you'll be doing

• Own and drive multiple features in recommendation and ranking services (Go and Python), from discovery through release and operation, including low-latency serving paths and model-serving integrations
• Drive architectural decisions: assess system lifecycle, fix deficiencies early, write clear RFCs and ADRs, and build consensus by presenting pros and cons
• Break complex, ambiguous initiatives into milestones delivering early and frequent impact, and parallelize work when possible
• Improve observability, scalability, cost efficiency, security, and code quality; resolve vulnerabilities within SLA; handle on-call incidents with quality post-incident reports
• Mentor less experienced engineers, participate in hiring, and collaborate with data scientists, machine learning engineers, and product managers to bring models and experiments safely into production
• Contribute to team development processes by recommending coding standards, frameworks, tools, and evangelizing effective engineering practices

Requirements

• Strong backend engineering experience in Go and/or Python, building and owning production services including stateful APIs, gRPC/REST integrations, and high-throughput request paths
• Experience with event streaming technologies (Kafka, Flink, Pub/Sub) and infrastructure as code tools such as Terraform
• Experience operating mission-critical services in production on AWS and/or GCP and Kubernetes, with observability, alerting, and on-call processes
• Proven ability to recommend and evangelize engineering best practices and tools, including understanding the strengths and limitations of agent-assisted development
• Deep knowledge of frameworks and tools used by the team, applied to critical decisions and problem solving beyond documentation
• Track record of sound architectural trade-offs explained clearly to both engineers and non-engineers; ability to work autonomously on ambiguous problems, slicing them into releasable increments with business impact considerations
• Mentoring mindset and excellent written and verbal English communication skills

Nice to have

• Background in recommendations, ranking, search, personalization, or relevance engineering
• Experience with ML serving, feature stores, vector databases, or real-time feature pipelines
• Familiarity with A/B testing and experimentation platforms

Perks and benefits

• Hybrid working model with 2 days per week on-site collaboration at Berlin campus
• 27 days holiday plus an extra day on 2nd and 3rd year of service
• Support for career growth with €1,000 educational budget, language courses, parental support, and access to Udemy Business platform
• Health and wellness benefits including health checkups, meditation, and gym access
• Financial benefits such as Employee Share Purchase Plan, sabbatical bank, public transportation ticket discount, life & accident insurance, and corporate pension plan
• Food benefits including digital meal and food vouchers
• Relocation support and resources for moving to Berlin