Machine Learning Engineer

Our Expectations:

• Expertise in designing and implementing complex IT systems.
• Ability to develop user-friendly, versatile tools.
• Proficiency in at least one programming language, such as Python, C++, Java, or Scala, along with expertise in Linux.
• Strong skills in evaluating and optimizing system performance, from initial design through to production troubleshooting.
• Deep understanding of algorithms and data structures.
• Initiative and creativity to improve existing solutions.
• Ability to work effectively both within and across teams.

Additional Advantages:

• Previous experience in the field of Machine Learning is not required, but would be an asset.
• Solid foundation in mathematics.
• Experience with GPU programming and Machine Learning frameworks such as Torch, PyTorch, or TensorFlow.
• Proven experience with distributed systems.
• Familiarity with Big Data technologies such as Hadoop, Kafka, Storm, Spark, or Flink.
• Hands-on experience with Google Cloud Platform (GCP) or similar cloud providers.

In Return, We Offer:

• A highly competitive salary.
• The opportunity to work with a team of enthusiasts experienced in Machine Learning, Big Data, and distributed systems, who are eager to share their knowledge and skills.
• Flexible working hours, with the possibility of remote work or working from our office in Warsaw.
• Access to the latest technologies, with the opportunity to apply them in a large-scale and fast-paced project.
• An opportunity to apply your expertise in optimizing algorithms that support hundreds of millions of internet users and billions of ad views per month within the RTB model.
• The ability to see the immediate impact of your work on the company's business outcomes.
• The possibility of publishing your results.

Daily Responsibilities:

• Developing and maintaining the ML training platform and the bidding infrastructure that evaluates ML models in the production environment.
• Identifying performance bottlenecks and optimizing critical, low-level parts of the system.
• Ensuring the reliability and scalability of implementations, and creating performance and correctness tests for new system components.
• Testing and benchmarking open-source Big Data and ML technologies to assess their suitability for the production environment.