Description:

Our client is a fast-growing data distribution strategy company that has recently secured $5M in funding to revolutionize AI-driven data infrastructure.
They are building a global team to develop cutting-edge solutions for seamless, secure, and scalable data exchange.
The company is seeking a Machine Learning Engineer to help design, train, and deploy high-performance ML models that drive intelligent automation, predictive analytics, and data optimization.
This position offers an opportunity to join a well-funded startup at the forefront of AI and data strategy.

Requirements:

Candidates must have 3+ years of experience in machine learning, deep learning, or AI model development.
Strong Python skills are required, with proficiency in TensorFlow, PyTorch, Scikit-learn, and NumPy.
Experience with big data frameworks such as Spark, Dask, Kafka, or Ray is necessary.
Candidates should have cloud expertise, with hands-on experience in AWS SageMaker, GCP Vertex AI, or Azure ML.
Familiarity with SQL, NoSQL, and distributed data storage solutions like Snowflake, BigQuery, MongoDB, or Redis is required.
Experience with MLOps and deployment, including Docker, Kubernetes, and CI/CD pipelines for ML model deployment, is essential.
Strong problem-solving and research skills are needed to optimize ML models for real-world applications.

The company has secured $5M in funding, allowing you to be part of a well-funded, high-growth AI startup.
You will engage in cutting-edge ML work, building AI-driven data infrastructure that shapes the future of data distribution strategy.
The position offers top-tier compensation, with competitive salaries in the top 5% to 1% market range.
The role is fully remote, allowing you to work with elite engineers from around the world.
The company promotes a high-performance culture with no micromanagement or bureaucracy, focusing on results-driven innovation.