Machine Learning Engineer

Distil Labs GmbH; United States; Remote; USD 180,000 - 240,000; Full time

Benefits offered by this job

Remote work within European time zones

Periodic in-person team offsites

Job summary

Distil Labs seeks a senior ML engineer to own end-to-end language-model pipelines—from traces and synthetic data to distributed training and deployment. This role combines hands-on ML engineering with applied research, turning advances in distillation, data generation, and model improvement into repeatable pipelines.

The position emphasizes production systems, low-latency services, and scalable serving across cloud and on-prem GPU environments.

Qualifications

• At least five years of experience building and shipping machine-learning systems in production.
• Ability to interpret research papers and convert ideas into working experiments and pipelines.
• Experience with Python and modern machine-learning frameworks, distributed training, and GPU infrastructure.

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.

Skills

Python

Distributed training

ML frameworks

GPU infrastructure

Research interpretation

Production systems

Cross-functional collaboration

Tools

PyTorch

TensorFlow

Job description

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.