Machine Learning Engineer - LLM Systems & Research (Europe)

We are seeking a talented and experienced Machine Learning Engineer specializing in Large Language Model systems engineering and applied research. The successful candidate will be responsible for implementing and maintaining LLM-based system components, with opportunities to contribute to research initiatives in educational and research domains.

You will be working on production-grade LLM systems that serve research institutions and educational organizations. This includes contributing to scalable inference systems under senior guidance, optimizing model performance, building robust ML pipelines, and contributing to applied research in educational AI. The role emphasizes engineering excellence with research-driven innovation.

1. ML Systems Support: Implement and maintain components of LLM inference systems under senior guidance, including model optimization and performance monitoring.
2. Model Deployment: Deploy and configure model serving solutions using established frameworks. Support optimization efforts for latency and throughput.
3. MLOps Implementation: Contribute to ML pipeline development and maintenance. Implement monitoring and evaluation components.
4. Research Support: Support research initiatives by implementing prototypes and helping translate research concepts into production-ready components.

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Software Engineering, or related field
• 2-4 years of Machine Learning Engineering experience
• Experience with LLM frameworks and deployment (production experience preferred but not required)

• Programming & ML: Proficient Python, experience with PyTorch/TensorFlow and Transformers
• Deployment: Experience with Docker, cloud platforms, and basic model serving
• Software Engineering: Testing, API design, version control

1. Interest in model optimization and performance tuning
2. Exposure to research environments or educational AI applications
3. Experience with monitoring and basic performance optimization
4. Previous work in EdTech, learning platforms, or academic settings

• System Components: Develop and maintain components of LLM serving infrastructure
• Platform Features: Implement features for learning platforms with mentorship
• Pipeline Components: Build data processing and monitoring pipeline components

• Languages: Python, Rust, SQL, YAML/JSON
• ML Stack: PyTorch, Transformers, ONNX, TensorRT, Distributed training frameworks
• Infrastructure: Kubernetes, Docker, Cloud platforms (AWS/GCP/Azure), GPU clusters
• Data: PostgreSQL, Redis, Vector databases, S3/GCS, Data streaming (Kafka/Kinesis)
• Monitoring: Prometheus, Grafana, ELK stack, MLflow, Weights & Biases