Applied Machine Learning Engineer, Industry Solutions
Core
Build and deliver end-to-end machine learning solutions for industrial clients, integrating classical ML with quantum layers for time series, optimization, and generative AI.
Role type
Applied Machine Learning Engineer
Builds
End-to-end ML pipelines and hybrid quantum-classical solutions for industrial clients
Domain
Quantum computing and industrial machine learning
Deliverable
production ML models
Required skills
Python, classical machine learning (tree-based methods, boosting, deep learning), rigorous experimental design, software engineering fundamentals, data cleaning and leakage protection
Preferred skills
Quantum computing concepts, applied ML verticals (time series, NLP, computer vision, optimization), quantum frameworks (PennyLane, Qiskit, Cirq)
Technologies
XGBoost, LightGBM, PyTorch, TensorFlow, NumPy, pandas, scikit-learn, Git
Responsibilities
Design end-to-end ML pipelines for client problems, choose appropriate classical methods based on data characteristics, treat quantum layers as constrained components in hybrid pipelines, perform data cleaning and statistical significance testing, contribute to internal ML libraries, translate quantum algorithms into testable implementations
Seniority
Mid-level, hands-on IC
