Machine Learning Research Engineer (AI)

TL;DR

Machine Learning Research Engineer (AI): Building and optimizing models that understand spatial relationships, visual diagrams, and unstructured collaboration, combining LLMs, Computer Vision, and GNNs. Focus on researching, prototyping, and shipping novel architectures for hirify.global's intelligent canvas.

Location: Candidates must be based in Amsterdam, Berlin, Yerevan, or London.

Company

hirify.global is a visual workspace for innovation, enabling distributed teams to build with its infinite canvas platform, serving over 100M users and 250,000 companies.

What you will do

• Design, train, and ship production-grade ML models, including deep learning, NLP, and computer vision systems.
• Conduct deep exploratory research on massive datasets to uncover novel patterns and predictive modeling opportunities.
• Apply advanced fine-tuning strategies to adapt state-of-the-art foundation models to specific domain tasks.
• Architect scalable ML pipelines for data processing, feature engineering, training, and evaluation.
• Optimize model performance for latency, throughput, and resource utilization, balancing complexity with production constraints.
• Collaborate cross-functionally to translate business requirements into technical ML specifications and integrate models.
• Champion MLOps excellence by automating deployment, implementing CI/CD, and establishing robust monitoring.
• Stay at the forefront of ML research, evaluating novel algorithms and techniques to drive innovation and technical strategy.

Requirements

• Strong foundation in ML theory and statistics, including hypothesis testing, probability distributions, regression, classification, and optimization.
• Solid engineering fundamentals with deep understanding of data structures, algorithms, and distributed system design.
• Deep proficiency in Python and modern ML stack, with hands-on experience using Pandas, NumPy, Scikit-learn, PyTorch, and TensorFlow.
• Expertise in PyTorch or JAX, including distributed training (DDP, FSDP) and debugging complex gradient issues.
• Ability to read, implement, and improve upon the latest academic papers (NeurIPS, ICML, CVPR).
• Track record of end-to-end ML delivery, from EDA to deploying models in production.
• Experience with large-scale systems, capable of designing resilient architectures for vast datasets and high-throughput inference requests.
• Minimum 3+ years of professional ML engineering experience with a Bachelor's/Master's degree, or 6+ years of industry experience without a formal degree.

Culture & Benefits

• Competitive equity package.
• Health insurance (London).
• Corporate pension plan (London).
• Lunch, snacks, and drinks provided in the office.
• Wellbeing benefit and WFH equipment allowance.
• Annual learning and development allowance.
• Opportunity to work for a globally diverse team.