Machine Learning Engineer

Role overview Own the full lifecycle of task-specific language models, from customer traces and synthetic-data creation through distributed training, evaluation, and production deployment. This senior role combines hands-on machine-learning engineering with applied research, turning advances in distillation, data generation, and model improvement into repeatable pipelines.

Responsibilities - Build end-to-end workflows for trace processing, synthetic-data generation, validation, fine-tuning, evaluation, and deployment. - Partner with research colleagues to test new methods and productionize approaches that improve customer models. - Create benchmarking and evaluation systems that measure quality, latency, and meaningful model improvement. - Run and optimize distributed training across cloud and on-premises GPU environments. - Operate workflow orchestration, container platforms, and secure multi-tenant model-serving systems. - Help maintain low-latency services that remain reliable under load.

Requirements - At least five years of experience building and shipping machine-learning systems in production. - Practical ownership of model training, evaluation, and deployment. - Ability to interpret research papers and convert ideas into working experiments and pipelines. - Strong experimental discipline, including designing evaluations that distinguish real improvements from noise. - Experience with Python and modern machine-learning frameworks, distributed training, and GPU infrastructure. - Comfort working across research, engineering, and production operations.

Nice to have - Experience with knowledge distillation, synthetic data, model self-improvement, or small language models. - Research publications or contributions to open-source machine-learning infrastructure.

Benefits and work setup - Remote work within European time zones, with periodic in-person team offsites. - Significant ownership of technical decisions, model quality, and production outcomes.