Machine Learning Engineer (100% remote)

• Collaborate with data scientists and analysts to design and deploy innovative models and machine learning systems.
• Develop and implement comprehensive solutions covering the entire machine learning model lifecycle, from data exploration and feature engineering to building ML pipelines and deploying models in production. This includes working on both batch and real-time models and providing operational support.
• Create scalable, efficient, and automated workflows for data analysis, model development, validation, and deployment.
• Develop high-performance, scalable software to deliver products in an iterative, continuous-release environment.
• Design and optimize data pipelines to support machine learning workflows effectively.
• Advocate for and implement robust software engineering practices within the team, focusing on cloud-native solutions for ML pipelines.
• Leverage and contribute to shared best practices within the community to enhance team capabilities.

• 100-130 net+VAT/h (mid); 130-150PLN net+VAT/h (senior), B2B via Devire
• Remote work model
• Benefits: Multisport card, private medical care, life insurance
• Long-term cooperation
• Flexible working hours, Polish time zone

We seek an individual who embodies a technology-agnostic mindset and a passion for lifelong learning, exploring cutting-edge scientific concepts. This role suits someone eager to blend algorithm design and software engineering expertise with mentoring, individual contribution, and strategic collaboration, bringing creativity, empathy, and a positive outlook to solving large-scale challenges.

• Bachelor’s or advanced degree in engineering, computer science, mathematics, or a related discipline.
• Mid-level roles require 3+ years, and senior roles require 5+ years of experience developing and deploying machine learning systems in production.
• Hands-on experience with big data tools such as Spark, Hadoop, or Kafka.
• Proficiency in at least one cloud platform (AWS, GCP, Azure) and familiarity with serverless code development, with a preference for GCP.
• Strong command of object-oriented or functional scripting languages, with Python as a must-have.
• Proficiency in Python data-handling libraries like Pandas or PySpark.
• Competence in SQL for data transformation and consumption; familiarity with dialects like SparkSQL or BigQuery SQL is a plus.
• Solid understanding of standard software engineering practices, including unit testing, test automation, CI/CD pipelines, code reviews, and design documentation.
• Experience with native ML orchestration tools such as Kubeflow, Vertex AI Pipelines, Airflow, or TFX.