ML Engineer

2 Weeks ago • 3 Years + • Artificial Intelligence

Job Summary

Job Description

Develop and deploy high-performance ML models for data distribution, anomaly detection, and predictive analytics. Build scalable ML pipelines using Python, TensorFlow, PyTorch, and cloud-based platforms. Optimize data processing with large-scale datasets using Spark, Dask, or Ray. Integrate ML models into production systems using APIs, microservices, and containerized environments (Docker, Kubernetes). Experiment with new approaches and improve model performance for accuracy, efficiency, and scalability. Requires 3+ years of experience in ML/DL/AI model development, strong Python skills (TensorFlow, PyTorch, Scikit-learn, NumPy), experience with big data frameworks (Spark, Dask, Kafka, or Ray), cloud expertise (AWS SageMaker, GCP Vertex AI, or Azure ML), and familiarity with SQL, NoSQL, and distributed data storage.
Must have:
  • 3+ years ML/DL/AI experience
  • Strong Python skills (TensorFlow, PyTorch)
  • Big data frameworks (Spark, Dask)
  • Cloud expertise (AWS, GCP, Azure)
  • SQL, NoSQL, distributed data storage
  • MLOps & Deployment (Docker, Kubernetes)
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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