ML Engineer

1 Month ago • 3 Years + • Artificial Intelligence

Job Summary

Job Description

The Machine Learning Engineer will design, train, and deploy high-performance ML models for data distribution, anomaly detection, and predictive analytics. Responsibilities include building scalable ML pipelines using Python, TensorFlow, PyTorch, and cloud-based platforms; optimizing data processing with large-scale datasets using Spark, Dask, or Ray; integrating ML into production systems using APIs, microservices, and containerized environments; and experimenting with new approaches to improve model performance. The role requires 3+ years of experience in ML/AI model development, strong Python skills (TensorFlow, PyTorch, Scikit-learn, NumPy), experience with big data frameworks (Spark, Dask, Kafka, or Ray), and cloud expertise (AWS SageMaker, GCP Vertex AI, or Azure ML).
Must have:
  • 3+ years ML/AI experience
  • Strong Python (TensorFlow, PyTorch)
  • Big data frameworks (Spark, Dask)
  • Cloud expertise (AWS, GCP, Azure)
  • MLOps & Deployment (Docker, Kubernetes)
  • SQL, NoSQL, distributed data storage
Perks:
  • $5M in funding
  • Cutting-edge ML work
  • Top-tier compensation
  • Fully remote & global team
  • High-performance culture

Job Details

Description

About Our Client

Our client, a fast-growing data distribution strategy company, 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.

They are 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 is an opportunity to join a well-funded startup at the forefront of AI and data strategy.

Requirements

What You’ll Do

  • Develop & Deploy ML Models – Design and optimize machine learning models for data distribution, anomaly detection, and predictive analytics.
  • Build Scalable ML Pipelines – Implement end-to-end data processing workflows for real-time and batch inference using Python, TensorFlow, PyTorch, and cloud-based ML platforms.
  • Optimize Data Processing – Work with large-scale datasets using Spark, Dask, or Ray to enhance data ingestion, transformation, and storage efficiency.
  • Integrate ML into Production Systems – Deploy ML models into production using APIs, microservices, and containerized environments (Docker, Kubernetes).
  • Experiment & Innovate – Stay ahead of ML advancements, test new approaches, and improve model performance for accuracy, efficiency, and scalability.

What Our Client is Looking For

  • 3+ years of experience in machine learning, deep learning, or AI model development.
  • Strong Python skills – Proficiency in TensorFlow, PyTorch, Scikit-learn, and NumPy.
  • Experience with big data frameworks (Spark, Dask, Kafka, or Ray).
  • Cloud expertise – Hands-on experience with AWS SageMaker, GCP Vertex AI, or Azure ML.
  • Database & Data Processing – Familiarity with SQL, NoSQL, and distributed data storage (Snowflake, BigQuery, MongoDB, or Redis).
  • MLOps & Deployment – Experience with Docker, Kubernetes, and CI/CD pipelines for ML model deployment.
  • Strong problem-solving and research skills – Ability to optimize ML models for real-world applications.

Benefits

Why Join Our Client?

$5M in funding – Be part of a well-funded, high-growth AI startup.
Cutting-Edge ML Work – Build AI-driven data infrastructure shaping the future of data distribution strategy.
Top-Tier Compensation – Competitive salaries in the top 5% to 1% market range.
Fully Remote & Global Team – Work with elite engineers from around the world.
High-Performance Culture – No micromanagement, no bureaucracy—just results-driven innovation.

📩 Apply Now – Send your resume, GitHub, and any ML projects you’ve worked on.

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