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Systems Engineer at CO-WORKER TECHNOLOGY
Norrtälje kommun, , Finland -
Full Time

Start Date

Immediate

Expiry Date

02 Feb, 26

Salary

0.0

Posted On

04 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Systems Engineering, Personalization, Search, Ranking Systems, Python, SQL, PyTorch, TensorFlow, Kafka, Kinesis, Spark, Flink, Feature Stores, Embeddings, Experiment Analysis, Metrics Trade-offs

Industry

Staffing and Recruiting

Description
The role • Design and ship personalization and search/ranking systems (candidate generation, ranking and re-ranking). • Build real-time feature pipelines and embeddings; maintain feature stores and user/item models. • Develop online inference services with strict latency/availability SLOs and robust A/B experimentation. • Define offline/online evaluation metrics (CTR/Conversion/NDCG, calibration, counterfactuals) and guardrails. • Partner with Product, Data and Platform teams to instrument events and drive measurable impact. What you bring • MSc/BSc in Computer Science, Applied Mathematics, Statistics or similar. • 5+ years in recommender systems, search or large-scale ML engineering. • Strong Python and SQL; experience with PyTorch or TensorFlow for training and evaluation. • Experience with streaming and near-real-time data (Kafka/Kinesis, Spark/Flink) and low-latency serving. • Exposure to feature stores, embeddings, vector retrieval (pgvector, Pinecone, Weaviate). • Solid understanding of evaluation design, experiment analysis and metrics trade-offs. • Professional English; Swedish or Finnish is a plus. Nice to have • Bandits/exploration, reinforcement learning or knowledge-graph-based recommenders. • Privacy, fairness and bias mitigation techniques in personalization. • End-to-end product experience (journeys, notifications, search, feed). • Cost optimization and caching strategies at scale.
Responsibilities
The role involves designing and shipping personalization and search/ranking systems, as well as building real-time feature pipelines and maintaining user/item models. Additionally, the engineer will develop online inference services and partner with various teams to drive measurable impact.