Jobgether
Staff Engineer - Recommendations
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Staff Engineer - Recommendations
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff Engineer - Recommendations based in United Kingdom.
This role focuses on building the data and machine learning foundations that power personalized discovery and social experiences at scale.
- Help evolve recommendation systems that connect users with relevant content, communities, groups, and events.
- Design systems capable of processing large volumes of platform-generated data.
- Collaborate closely with data scientists, product managers, and engineers to turn behavioral signals into meaningful user experiences.
- Advance personalization from simple heuristics toward increasingly sophisticated, data-driven recommendations.
- Make a direct impact on a highly interactive consumer platform within a distributed, collaborative engineering environment.
Accountabilities
- Design, develop, maintain, and optimize scalable data pipelines, backend services, and APIs supporting recommendations, content discovery, groups, events, and other data-driven experiences.
- Build data models and schemas that support both analytical workloads and real-time personalization and recommendation systems.
- Partner with data scientists, product managers, and engineering teams to ensure relevant user and platform data is accurately captured, processed, and made available for product experiences.
- Develop and maintain large-scale data processing workflows using technologies such as Spark and Kafka.
- Help evolve recommendation capabilities from basic heuristics toward more sophisticated, data-backed personalization models.
- Contribute to backend architecture and implementation, including REST and WebSocket APIs, caching systems, queueing infrastructure, and cloud orchestration.
- Process and transform high-volume platform data into reliable datasets and signals that can support machine learning and personalization use cases.
- Optimize data storage, processing, and database performance for both analytical workloads and high-throughput real-time applications.
- Collaborate across a full-stack engineering environment to deliver reliable, scalable features from data layer through user-facing experiences.
- Contribute to technical strategy and the evolution of engineering and product capabilities as recommendation and personalization needs grow.
- Monitor production systems and participate in incident response, including occasionally supporting urgent troubleshooting during outages.
- Promote strong engineering practices around scalability, reliability, maintainability, observability, and data quality.
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Requirements
- 3+ years of professional software engineering experience, with a strong focus on data engineering, backend systems, or scalable SaaS and online platforms.
- Proven experience designing, building, and optimizing production-grade ETL/ELT data pipelines.
- Strong SQL skills, including database optimization for analytical workloads and high-throughput real-time access.
- Hands-on experience with big data technologies such as Spark, Kafka, Hadoop, or Beam.
- Experience working with cloud platforms at scale, particularly AWS or Google Cloud.
- Programming experience across technologies such as Python, JavaScript/Node.js, MongoDB, and Redis, with the ability to work effectively across multiple languages and systems.
- Experience with Elasticsearch, data warehousing, and machine learning systems.
- Strong understanding of scalable backend architecture, distributed data processing, APIs, caching, queues, and cloud infrastructure.
- Ability to collaborate effectively with data scientists, product managers, engineers, and other cross-functional stakeholders.
- Strong communication skills and an agile, collaborative mindset suited to a distributed engineering environment.
- Bonus experience with content discovery, recommendation engines, personalization, social graphs, online communities, or user-generated content.
- Experience building consumer products, e-commerce platforms, marketplaces, or social products is highly valued.
- Interest or experience in virtual reality, online communities, or creator-driven platforms is a plus.
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Benefits
- 100% remote work with flexible working hours and designated core collaboration hours.
- Health benefits.
- 401(k) plan for eligible U.S. employees.
- Stock options.
- Generous paid holiday schedule.
- Unlimited and flexible vacation time.
- Paid parental leave.
- Opportunity to work on large-scale recommendation, data, and personalization systems.
- Collaborative and distributed environment where engineers can contribute to projects and influence technical direction.
How Jobgether Works
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Why Apply Through Jobgether?
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
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