Machine Learning Engineer (RecSys for Social Networks) in Fireart Studio

We are looking for Machine Learning Engineer to join the client's project.

The client is a young company with big ambitions, and they are looking for someone who shares their passion for building something new and exciting. They believe that their success will be driven by their ability to attract and retain the best talent, and they are committed to creating a work environment that is challenging, rewarding, and fulfilling.

If you are excited about the opportunity to help build a new social media platform from the ground up, and if you are passionate about technology, deep learning, AI, product development, and building great teams, then we would love to hear from you.

As a Machine Learning Engineer, you will play a vital role in shaping and building our modern, collaborative, social network. You will be responsible for developing, implementing, and optimizing models that drive core features and services within the app. You will work with large and complex data sets to derive insights that inform product development and our business strategies. You will work closely with product, engineering, and commercial teams to integrate and maintain these models.

You Will:

• Be a specialist in Recommender Systems, with practical experience building and deploying modern Recommender Systems at scale.
• Have an excellent understanding of the field of Recommender Systems, especially the recent developments over the last 5 years and the future trajectory of the field.
• Develop, implement, and optimise machine learning models to support core Pynea features and services.
• Develop and deploy state-of-the-art recommendation algorithms using Python and relevant libraries (e.g., TensorFlow/PyTorch/Keras/ etc) in the Python ecosystem.
• Deploy models and make them accessible via. API to be consumed by the Pynea backend.
• Have strong experience in DevOps combined with AWS (or equivalent GCP) technologies such as Docker, Kubernetes, EC2, ECR, ECS, Glue, Lambda, S3, Cloud Formation, Cloudwatch etc.
• Have proven experience deploying models and ML pipelines at scale, including exprience using AWS SageMaker and/or Google Cloud AI.
• Support and drive implementation of features around NLP and LLMs.
• Tackle challenges related to user recommendation/matching, suspicious usage patterns, content discovery, tag mapping and data categorisation.
• Analyse large and complex data sets to derive valuable insights that inform product development and business strategies.
• Benchmark algorithms and models for data-driven product iteration. Using standard metrics and live user data.

You Will Thrive if You Have:

• Have 3+ years industry experience as a Machine Learning Engineer, Data Engineer, DevOps or MLOps Engineer, specialising in deploying machine learning pipelines at scale.
• Care deeply about AI, Deep Learning and its ability to craft seamless product experiences with direct real world applications.
• Experience deploying pipelines for recommendation systems, including online deployment and real-time updates.
• Experience with large distributed systems, deep learning, neural networks and/or natural language processing.
• Enjoy reading and implementing recent research papers.
• Strong problem-solving skills and ability to think algorithmically.
• Excellent communication skills and can effectively collaborate with cross-functional teams.

This Role Is Not For You If:

• You live in the realm of theory but have not demonstrated being able to operationalise it.
• You require significant direction and hand-holding.
• You're unsure of your ability to deliver category defining work.

Bonus Experience:

• You have worked in Big Tech, SaaS, Eccom, or an Ads Network Previously
• Graph data structures, path-finding and link prediction algorithms
• You have a startup mentality
• Experience with AWS.
• Familiarity with the social networking or B2C space.
• Degree in Computer Science, Data Science, Mathematics, or a related field.

What we offer:

💸 Competitive compensation dependent on experience and skills

👭 A friendly team of like-minded individuals

🤒 Compensation for sick leave

🌴 21 working days of paid vacation, plus all Polish national holidays

🎉 Corporate events and activities

🩺 Private medical care (for residents in Poland)

🧩 Opportunities for learning and development