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Information Technology 🏢 Full Time ⭐️ Verified

AI/ML Engineer

QuantumLeap Dynamics
Austin
Estimated Salary
USD 180.000 – USD 250.000
Live Update
14 Mei 2026
Deadline
14 Mei 2027

Job Description

Join QuantumLeap Dynamics at the forefront of 2026's technological revolution. We're pioneering autonomous AI systems that redefine human-machine collaboration. As an AI/ML Engineer, you'll architect neural networks that learn, adapt, and solve problems in real-time across healthcare, logistics, and sustainability sectors. Our Austin hub offers a state-of-the-art lab where your innovations will directly impact global industries. If you thrive at the intersection of bleeding-edge research and practical application, this is your opportunity to shape tomorrow's intelligence today.

Responsibilities

  • Design and deploy production-grade ML models for edge computing and cloud-native environments
  • Develop reinforcement learning frameworks for autonomous decision-making systems
  • Optimize neural architectures for low-latency inference in resource-constrained devices
  • Create ethical AI governance protocols ensuring bias mitigation and transparency
  • Collaborate with quantum computing teams to hybridize classical and quantum ML algorithms
  • Lead cross-functional sprints integrating AI solutions with IoT and blockchain ecosystems
  • Publish research in top-tier ML conferences and contribute to open-source frameworks

Qualifications

  • PhD in Computer Science/ML or equivalent with 5+ years of industry experience
  • Expertise in PyTorch/TensorFlow and distributed training paradigms
  • Proven track record deploying models at scale (Kubernetes, AWS/GCP)
  • Deep understanding of transformer architectures and diffusion models
  • Published work in NeurIPS/ICML or equivalent top-tier venues
  • Experience with MLOps pipelines (MLflow, Kubeflow)
  • Strong background in causal inference and federated learning

Required Skills

Python TensorFlow PyTorch Kubernetes AWS Reinforcement Learning Neural Networks MLOps Quantum Computing

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