Machine Learning Platform Engineer

Technologies we use

About the project

Your responsibilities

• Build and operate the ML infrastructure and platforms powering production AI products.
• Design systems for model training, evaluation, deployment, inference, and experimentation.
• Build and optimize model-serving and inference infrastructure for high-throughput, low-latency workloads.
• Improve the reliability, scalability, latency, throughput, and cost efficiency of AI systems.
• Develop reliable pipelines for data preparation, training, evaluation, model releases, and continuous improvement.
• Build platforms and tooling that enable AI engineers and researchers to experiment, evaluate, and ship models faster.
• Develop evaluation and benchmarking infrastructure to measure model quality, performance, and regressions.
• Build production observability, monitoring, tracing, and alerting for AI and ML workloads.
• Identify bottlenecks across the ML stack and continuously improve system performance.
• Work closely with AI engineers, researchers, and product teams to turn evolving model requirements into production-ready infrastructure.

Our requirements

• Strong software engineering fundamentals and experience building production systems.
• Experience building ML infrastructure, ML platforms, or production machine learning systems.
• Hands-on experience with model deployment, inference, evaluation, or data pipelines.
• Experience operating ML or AI workloads in production.
• Strong understanding of distributed systems, scalability, observability, and system reliability.
• Familiarity with GPU-based training or inference infrastructure.
• Ability to write clean, maintainable, production-quality code.
• Ability to diagnose performance, reliability, and cost bottlenecks across the ML stack.
• Comfort working in ambiguous, fast-moving environments.
• Bias toward ownership, experimentation, and continuous improvement.

Optional

• AI infrastructure reliably supports production workloads at scale.
• Models can be trained, evaluated, deployed, and improved efficiently.
• Inference systems deliver strong latency, throughput, reliability, and cost efficiency.
• ML pipelines are reproducible, observable, maintainable, and robust.
• Model and infrastructure regressions are detected quickly and diagnosed efficiently.
• Common ML infrastructure capabilities become reusable platform components instead of being rebuilt for every AI product.
• The AI stack can evolve rapidly as new models, architectures, and inference techniques emerge.

What we offer

• Polish employment contract.
• Fully remote position for candidates based in Poland.
• You will join an existing global team and collaborate with colleagues across different regions.
• There is no fixed company-wide schedule or requirement to work in one specific time zone.
• Sufficient overlap with your immediate teammates is required for effective collaboration.
• A company laptop will be provided where required for the role.
• The compensation package consists of a base salary and equity.
• Compensation is assessed individually based on experience, technical capability, expected impact, and relevant market benchmarks.
• Candidates are invited to share their expected gross monthly salary in PLN.