Machine Learning Engineer

Cosmos; New York (NY); On-site; USD 140,000 - 190,000; Full time

Benefits offered by this job

Health benefits

PTO + holidays

MacBook Pro

Tech stipend

Team events

Commute covered NYC

Lunch/dinner stipend

Superpower membership

Job summary

Cosmos, based in New York City, is seeking a Machine Learning Engineer specializing in personalization and recommendation systems to join our engineering team in New York City or Warsaw.

You will design, develop, and scale models that personalize content for millions of users, collaborate with product teams, and own end-to-end ML pipelines from data exploration to deployment, with a strong focus on real-time inference and performance.

Qualifications

• 5+ years designing and deploying large-scale personalization and recommendation systems.
• Experience with real-time inference and latency optimization.
• Proficient in Python and cloud platforms (AWS, SageMaker, ECS, EKS).

Responsibilities

• Architect, develop, and scale personalization and recommendation models.
• Collaborate with product engineering to run experiments and improve quality.
• Own end-to-end deployment of ML pipelines from data exploration to deployment.
• Mentor team members and stay current with industry trends.

Skills

Personalization

Recommendation systems

Python

TensorFlow

PyTorch

Real-time inference

Collaborative filtering

Embeddings

Cloud platforms

Tools

TensorFlow

PyTorch

Spark

Flink

Kafka

Snowflake

SageMaker

ECS

EKS

Job description

About the role...

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.

What you'll do:

• Architect, develop, and scale state-of-the-art personalization and recommendation models leveraging deep learning, collaborative filtering, embeddings, and real-time inference.
• You’ll 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.

What we're looking for:

• 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

Benefits & perks:

• Premium health, dental, andvision
• 20 days PTO, plus company holidays, plus a 2-week winter break
• Top-spec MacBook Pro, Apple Studio Display
• Monthlystipendforsoftwareandtools
• Monthly team events
FullycoveredcommuteinNYC
• Daily lunch & dinnerstipend atthe office
• Optional Superpower membership