Machine Learning Engineer

Cosmos•New York, NY

1d•Hybrid

About The Position

Cosmos is looking for a Machine Learning Engineer specializing in personalization and recommendation systems to join our engineering team in New York City or Warsaw. You'll be part of our team that develops cutting-edge machine learning models that enhance user experience, drive engagement, and personalize content for millions of users. You should have extensive experience in designing and implementing machine learning systems, specifically personalization and recommendation models, that can scale reliably and handle rapid user growth. As Cosmos continues to scale, this role will be crucial in addressing challenges related to growing data volumes, optimizing our ML infrastructure for increased demand, and delivering highly personalized, seamless user experiences. A core part of this role will involve striking the balance between delivering highly aesthetic, curated content and ensuring users feel deeply connected to the recommendations they receive.

Requirements

Significant hands-on experience (5+ years) designing, implementing, and deploying large-scale recommendation and personalization systems in production.
Expert knowledge of ML frameworks and tools such as TensorFlow, PyTorch, or similar.
Proven experience optimizing model performance and latency for real-time applications.
Familiarity with large-scale data systems (Spark, Flink, Kafka, Snowflake, etc.).
Strong coding skills in Python and familiarity with cloud platforms like AWS, particularly SageMaker, ECS, and EKS.
Excellent communication skills, with experience aligning ML strategies to broader company objectives.

Responsibilities

Architect, develop, and scale state-of-the-art personalization and recommendation models leveraging deep learning, collaborative filtering, embeddings, and real-time inference.
Be embedded on the product engineering team, collaborating with engineers and company leads to design experiments, evaluate performance, and continuously improve recommendation and personalization quality.
Own end-to-end deployment of ML pipelines, from data exploration and prototyping to deployment, monitoring, and iteration.
Mentor team members, fostering growth in ML expertise across Cosmos engineering.
Stay current with industry trends, actively applying insights and techniques from leading research to practical business solutions.