Senior ML Engineer

We are , a global software company transforming how businesses run. Our product suite can adapt to the needs of diverse industries and use cases within one powerful platform, empowering ~250,000 customers worldwide to reimagine how work gets done, drive greater efficiency, and scale like never before.

With over 2,900 employees across the globe, we grow by prioritizing transparency and knowledge sharing. We care about the impact you make, not the hours you clock, so we encourage initiative, ownership, and fresh thinking. We back our people with flexible work, wellness and mental health support, and a work environment built on collaboration.

About the role : We're looking for an

ML Engineer - Infrastructure

to build the infrastructure that keeps our ML models

production-grade, cost-efficient, and reliable at scale

• . You'll work on everything from observability and pipelines to disaster recovery, ensuring that our AI investments are always serving customers in real time.

We fully embrace the AI revolution and equip you with AI-powered IDEs, customizable agent rules, prompt engineering tools, and AI-infused CI / CD pipelines designed to boost speed and reliability. You'll also tap into AI-driven insights, helping you make smarter decisions, faster.

The role is based in our Warsaw office - established in 2022, it is a growing hub for engineers who love solving impactful problems. Teams here work on a broad range of challenges that push the boundaries of our products and infrastructure.

What you'll do :

platform for model deployment

integrated with standards (authentication and authorization, observability, disaster recovery).

observability

, cost projections, and

release pipelines

with automated testing, canary deployments, and rollback strategies.

reliable, scalable, and always production-ready

fixing issues and handling alerts

production-grade services

future-facing efforts

: fine-tuning models on data, balancing cost vs. performance, and optimizing GPU / CPU usage.

performance optimization -

from tokenization pipelines to GPU parallelization strategies.

Your experience & skills :

ML / AI Engineer,

MLOps, Infrastructure Engineer, or DevOps with ML focus

machine learning models in production at scale

CI / CD pipelines, observability, testing, and rollback strategies

cloud infrastructure (AWS preferred)

, GPUs, and containerized services.

cost monitoring and optimization

for large-scale ML systems.

data scientists and ML engineers

, translating research into robust services.

fine-tuning models, managing training data, and balancing performance vs. cost trade-offs