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AI/ML Research Engineer (2026)

NeuroSync Technologies
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
Live Update
15 Mei 2026
Deadline
15 Mei 2027

Job Description

Join NeuroSync Technologies at the forefront of 2026's AI revolution! We're pioneering next-generation neural interfaces and autonomous systems that redefine human-machine collaboration. As an AI/ML Research Engineer, you'll architect breakthrough algorithms powering our quantum-optimized neural networks and adaptive learning platforms. Work in our state-of-the-art lab where your innovations directly shape the future of cognitive computing.

What you'll achieve:
- Develop novel deep learning architectures for real-time multimodal data processing
- Lead research in explainable AI and ethical autonomous systems
- Collaborate with neuroscientists to decode brain-computer interfaces
- Scale models to handle petabyte-scale edge computing workloads

Responsibilities

  • Design and implement cutting-edge ML pipelines for predictive analytics
  • Optimize neural networks for quantum computing environments
  • Conduct A/B testing on adaptive learning algorithms
  • Author whitepapers on federated learning advancements
  • Mentor junior engineers in MLOps best practices
  • Secure patents for novel AI hardware-software co-designs
  • Interface with hardware teams for neuromorphic chip integration

Qualifications

  • PhD in Computer Science/ML or equivalent experience
  • 5+ years with TensorFlow/PyTorch and distributed training
  • Expertise in reinforcement learning for robotics
  • Published research in top-tier AI conferences (NeurIPS, ICML)
  • Proficiency in quantum computing frameworks (Qiskit, Cirq)
  • Strong background in differential privacy and federated learning
  • Experience with neuromorphic computing hardware (Intel Loihi, IBM TrueNorth)
  • Security clearance or ability to obtain (for DoE projects)

Required Skills

Machine Learning Deep Learning Quantum Computing Neural Networks Reinforcement Learning TensorFlow PyTorch Qiskit MLOps Neuromorphic Computing

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