Company Mission

Constructor's mission is to enable all educational organisations to provide high-quality digital education to 10x people with 10x efficiency. With strong expertise in machine intelligence and data science, Constructor’s all-in-one platform for education and research addresses today’s pressing educational challenges: access inequality, tech clutter, and low engagement of students. Our headquarters is located in Switzerland, and we also have legal entities in Germany, Bulgaria, Serbia, Turkey, and Singapore.

Role Description

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.

Responsibilities

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

Required Qualifications

• Education & Experience: - 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)
• Technical Skills: - 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

Preferred Qualifications

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

What You’ll Build

• 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

Technical Environment

• 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

What We Offer

• Choice of work equipment (e.g., laptop, monitor, etc.)
• English classes (iTalki – $130 monthly)
• Flexible schedule (we usually work between 09:00/10:00 and 18:00/19:00 CET or EET)
• Newborn bonus (€500 per child)
• Patent remuneration
• Paid leave
• Remote work in locations without our offices
• Hybrid work in locations with offices: - Sofia: 59 G. M. Dimitrov Blvd., NV Tower, 8th floor, 1700
  • Belgrade: Makedonska 12, 11000 Belgrade, Serbia
  • Istanbul: Rüzgarlı Bahçe Mah., Kavak Sok., Smart Plaza B Blok 31/B, 34805 Kavacık-Beykoz/İstanbul
  • Sakarya: Esentepe Mh., Akademiyolu Sk., Teknoloji Geliştirme Bölgesi No. 10 D/206, Serdivan, Sakarya
  • Izmir: Ege Üniversitesi Kampüsü, Erzene Mah., Ankara Cad., No:172/67, 35100 Bornova/İzmir