ML Engineer

Description
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.

We're looking for a
Machine Learning Infrastructure Engineer
to design, build, and maintain the systems that keep our ML models production-grade, scalable, and cost-efficient. You'll develop and optimize the infrastructure behind our AI products, from observability and CI/CD pipelines to disaster recovery, ensuring that every model we serve is reliable and performant 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 that boost both speed and reliability. You'll also leverage AI-driven insights to make smarter operational 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.

About The Role

• Build and maintain a platform for model deployment integrated with standards (authentication and authorization, observability, disaster recovery).
• Design and implement observability, cost projections, and release pipelines with automated testing, canary deployments, and rollback strategies.
• Ensure ML models are reliable, scalable, and always production-ready.
• Serve as on-call support for ML models, fixing issues and handling alerts.
• Collaborate with AI engineers and data scientists to move research into production-grade services.
• Lead future-facing efforts: fine-tuning models on data, balancing cost vs. performance, and optimizing GPU/CPU usage.
• Drive continuous performance optimization - from tokenization pipelines to GPU parallelization strategies.

Requirements

• 5+ years of experience as an ML/AI Engineer, MLOps, Infrastructure Engineer, or DevOps with ML focus.
• Proven experience deploying and maintaining machine learning models in production at scale.
• Hands-on expertise in hosting, deploying, and managing the infrastructure of ML models, ensuring they are production-grade, scalable, and reliable.
• Strong knowledge of CI/CD pipelines, observability, testing, and rollback strategies.
• Hands-on expertise with cloud infrastructure (AWS preferred), GPUs, and containerized services.
• Experience with cost monitoring and optimization for large-scale ML systems.
• Comfort working with data scientists and ML engineers, translating research into robust services.
• Bonus: Experience with fine-tuning models, managing training data, and balancing performance vs. cost trade-offs.

What Can Offer You

• From Monday to Wednesday, we'll fuel your day with free breakfast and lunch in the office.
• Enjoy private medical care and Multisport card to keep you healthy.
• We've got you covered with life insurance.
• We care of employee's mental health as well - you will get access to Calm Mindfulness App.
• Get awesome discounts on our partners' products and services.
• Join in on our fun team events and get-togethers.
• Look forward to a little something special from us during your birthday and work anniversaries.
• For Online Learning opportunities, you will gain access to LinkedIn Learning.