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Information Technology 🏢 Full Time ⭐️ Verified

Senior Machine Learning Engineer

QuantumLeap Labs
San Francisco
Salary Estimate
USD 180.000 – USD 250.000
Latest
Live Update
25 Mei 2026
Deadline
25 Mei 2027

Job Description

Join QuantumLeap Labs at the forefront of AI innovation! We're seeking a visionary Senior Machine Learning Engineer to architect next-generation solutions that transform industries. Collaborate with elite researchers in our state-of-the-art downtown San Francisco lab, where cutting-edge algorithms meet real-world impact. Enjoy competitive compensation, flexible work arrangements, and unparalleled growth opportunities in one of tech's most dynamic ecosystems.

Responsibilities

  • Design and implement scalable ML pipelines for predictive analytics and NLP applications
  • Lead model development using TensorFlow, PyTorch, and distributed computing frameworks
  • Collaborate with cross-functional teams to deploy ML models into production environments
  • Conduct advanced A/B testing and statistical analysis to optimize model performance
  • Mentor junior engineers and establish best practices for ML development lifecycle
  • Research and integrate emerging AI technologies (e.g., federated learning, quantum ML)
  • Translate complex technical concepts into actionable business strategies

Qualifications

  • MS/PhD in Computer Science, Mathematics, or related technical field
  • 5+ years of hands-on ML engineering experience with production systems
  • Expertise in Python, SQL, and cloud platforms (AWS/GCP/Azure)
  • Strong foundation in deep learning, reinforcement learning, or computer vision
  • Proven track record of deploying ML solutions at scale (>1M users)
  • Experience with MLOps tools (Kubernetes, Airflow, MLflow)
  • Excellent problem-solving skills with ability to handle ambiguous challenges
  • Publication record in top-tier ML/AI conferences (NeurIPS, ICML, CVPR) preferred

Required Skills

Machine Learning Python TensorFlow PyTorch AWS Data Science AI NLP Deep Learning Distributed Systems MLOps

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