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Information Technology 🏢 Full Time ⭐️ Verified

AI/ML Engineer - 2026 Vision

QuantumLeap Labs
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
Live Update
15 Mei 2026
Deadline
15 Mei 2027

Job Description

Join QuantumLeap Labs at the forefront of 2026's technological revolution! We're pioneering quantum-ML fusion systems that will redefine computing. As our AI/ML Engineer, you'll architect next-generation neural networks, collaborate with Nobel Prize-winning researchers, and deploy solutions that impact billions. Enjoy unlimited learning stipends, flexible remote work, and equity in a unicorn-backed startup.

Why You'll Love Working With Us: We offer cutting-edge hardware access, patent bonuses, and quarterly innovation sabbaticals. Our culture blends Silicon Valley ambition with Scandinavian wellness principles.

Responsibilities

  • Design and deploy quantum-enhanced ML models for real-world applications
  • Lead research on neuromorphic computing architectures for 2026-era devices
  • Optimize edge AI systems for 5G/6G networks and IoT ecosystems
  • Mentor junior engineers in ethical AI development practices
  • Collaborate with product teams to commercialize breakthrough technologies
  • Develop MLOps pipelines for automated model lifecycle management
  • Present findings at global tech summits and publish in top-tier journals

Qualifications

  • PhD in Computer Science or equivalent with 5+ years ML experience
  • Expertise in PyTorch/TensorFlow with quantum computing frameworks (Qiskit, Cirq)
  • Published research in NeurIPS/ICML or similar tier conferences
  • Proven track record deploying models at 99.9% accuracy scale
  • Deep understanding of federated learning and differential privacy
  • Strong background in GPU/TPU optimization and hardware acceleration
  • Experience with autonomous systems and reinforcement learning
  • Portfolio demonstrating 2026-forward projects (e.g., generative AI ethics)

Required Skills

AI/ML Quantum Computing PyTorch TensorFlow MLOps Reinforcement Learning Federated Learning GPU Optimization

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