Machine Learning Engineer

3 dni temu

Poland Newxel Pełny etat

About the Role

We are looking for a Machine Learning Engineer (MLE) to design, build, and productionize ML evaluation and automation pipelines that support large-scale model development workflows.

The role involves working closely with algorithm developers, ML researchers, and product teams to build robust and scalable systems that automate model evaluations, manage distributed data processing, integrate human-in-the-loop validation processes, and produce actionable evaluation metrics.

This position offers full ownership over the system's architecture, implementation, scalability, and operational robustness, enabling efficient and rapid ML experimentation at scale.

Responsibilities


• Design and implement end-to-end ML evaluation pipelines supporting large-scale model assessments.


• Build distributed data processing pipelines for ML inference, pre-processing, post-processing, and data transformation tasks.


• Integrate human-in-the-loop annotation and validation systems as part of the evaluation workflow.


• Design and implement scalable job orchestration, task dispatchers, and worker-based architectures for parallel ML workloads.


• Manage storage, indexing, and retrieval of intermediate and final data artefacts.


• Develop monitoring, logging, alerting, and observability for the entire pipeline.


• Collaborate with algorithm and research teams to define flexible and reusable infrastructure components.


• Automate manual and ad-hoc processes into robust, production-grade workflows.


• Optimize resource utilization and ensure cost-effective pipeline operations.

Qualifications

Must Have:


• B.Sc. in Computer Science, Software Engineering, or a related technical field.


• 3+ years of production-level Python development.


• 3+ years building ML pipelines, data processing systems, or model evaluation infrastructure.


• Strong system design skills and hands-on experience building distributed, scalable systems.


• Experience with cloud compute platforms (Preferably Azure) — compute, storage, and networking.

Proficiency with:


• Containerised workloads (Docker, Kubernetes)


• SQL / NoSQL databases.


• Experience integrating data annotation platforms or human labeling workflows.


• Strong ownership mindset, able to independently design, build, and operate complex ML infra systems.

Why This Role?


• Full ownership of designing critical ML infrastructure from the ground up.


• Direct impact on ML development velocity and model quality.


• Work with experienced ML researchers and engineers on production-grade systems.


• Exposure to real-world applied AI workflows, cloud-scale systems, and automation challenges.

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