Join a cutting-edge AI company pioneering a new era in machine learning—drawing inspiration from quantum mechanics and human cognition. This team is transforming how models learn and infer by building a proprietary Quantum Cognition Machine Learning (QCML) framework that outperforms traditional methods in real-world, high-dimensional applications across finance, genomics, robotics, and more.

This is a rare opportunity to work on technology that bridges the gap between deep research and impactful engineering. You’ll contribute directly to ML experimentation, model benchmarking, and productization efforts—helping shape a next-generation AI platform that operates efficiently on classical hardware while solving some of the most complex data challenges today.

What You’ll Be Doing

Tech Breakdown

• 50% ML experimentation and benchmarking
• 30% Engineering and ML infrastructure development
• 20% Cross-functional collaboration with research and client teams
  
  

Daily Responsibilities

• Analyze structured and unstructured datasets, perform EDA, and prepare data for experimentation
• Build baseline models (Random Forest, XGBoost, Deep Neural Nets) to evaluate performance of proprietary ML technology
• Run reproducible experiments, track performance metrics (R², F1, AUROC, etc.), and generate insights
• Improve internal ML pipelines and infrastructure to support scalable PoC delivery
• Work with researchers and client-facing teams to ensure results align with business needs and timelines
• Help shape best practices in model deployment, experiment reproducibility, and toolchain optimization
  
  

Required Skills & Experience

• 4+ years of hands-on experience in machine learning
• Strong programming skills in Python and ML libraries (scikit-learn, PyTorch, JAX, or TensorFlow)
• Experience with model development, evaluation, and performance optimization
• Familiarity with ML fundamentals—classification, regression, metrics, and experimentation frameworks
• Proficient in data wrangling, version control (Git), and feature engineering
• Strong written and verbal communication skills; able to collaborate across technical and business teams
  
  

Bonus Qualifications

• Background in quantum mechanics or advanced linear algebra
• Experience with time-series, biological, or chemical data
• Exposure to cloud platforms (AWS, GCP, Azure)
• Familiarity with modern ML approaches such as active learning, reinforcement learning, or generative models
• Previous experience in client-facing or consulting roles
  
  

The Offer

• Competitive salary and benefits
• Remote-friendly role with the option to work onsite with a collaborative team
• Opportunity to work on a novel, paradigm-shifting ML framework
• High-impact work across industries including finance, defense, genomics, and robotics
• Significant opportunities for career growth, learning, and working closely with a world-class research and engineering team
  
  

Applicants must be currently authorized to work in the US on a full-time basis now and in the future.

Posted By: Fedro De Tomassi