About the Role
Join our AI & Machine Learning team to build the next generation of quantum-enhanced machine learning systems. As a Machine Learning Engineer specializing in Quantum ML, you'll develop hybrid quantum-classical algorithms, create quantum machine learning frameworks, and apply these cutting-edge techniques to solve real-world problems.
Key Responsibilities
- Algorithm Development: Design and implement quantum machine learning algorithms for classification, optimization, and generative modeling
- Hybrid Systems: Build hybrid quantum-classical ML pipelines that leverage the best of both worlds
- Framework Development: Contribute to our quantum ML framework and tooling infrastructure
- Model Deployment: Deploy quantum-enhanced ML models to production systems
- Research: Stay at the forefront of quantum ML research and translate academic findings into practical applications
- Collaboration: Work with quantum physicists, software engineers, and domain experts
- Optimization: Optimize quantum circuits and classical ML models for performance and accuracy
Required Qualifications
- Education: Master's or PhD in Computer Science, Machine Learning, Physics, or related field
- Experience: 4+ years of machine learning engineering experience
- ML Expertise:
- Strong foundation in machine learning theory and practice
- Proficiency with TensorFlow, PyTorch, or JAX
- Experience with deep learning, neural networks, and optimization
- Knowledge of classical ML algorithms and when to apply them
- Quantum Computing:
- Understanding of quantum computing principles and quantum algorithms
- Experience with quantum programming frameworks (Qiskit, PennyLane, Cirq, TensorFlow Quantum)
- Familiarity with variational quantum algorithms (VQE, QAOA, QNN)
- Programming: Expert-level Python programming skills
- Mathematics: Strong background in linear algebra, probability, and optimization
Preferred Qualifications
- Experience with quantum machine learning research or applications
- Published papers in quantum computing or machine learning conferences/journals
- Familiarity with quantum hardware platforms and their constraints
- Experience with distributed computing and cloud platforms (AWS, GCP, Azure)
- Knowledge of quantum chemistry, quantum optimization, or quantum finance
- Contributions to open-source quantum computing projects
- Experience with MLOps and model deployment pipelines
What You'll Build
- Quantum Neural Networks: Design parameterized quantum circuits for machine learning tasks
- Quantum Kernels: Implement quantum kernel methods for classification and regression
- Optimization Solvers: Build quantum-enhanced optimization algorithms for complex problems
- Generative Models: Develop quantum generative adversarial networks (QGANs) and quantum autoencoders
- Hybrid Pipelines: Create end-to-end ML pipelines that seamlessly integrate quantum and classical components
- Applications: Apply quantum ML to drug discovery, materials science, finance, and logistics
Technical Stack
- Quantum Frameworks: Qiskit, PennyLane, Cirq, TensorFlow Quantum
- ML Frameworks: PyTorch, TensorFlow, JAX, scikit-learn
- Programming: Python, C++ (for performance-critical components)
- Cloud: AWS, GCP (quantum computing services)
- Tools: Docker, Kubernetes, Git, Jupyter
- CI/CD: GitHub Actions, Jenkins
Why This Role is Exciting
- Cutting Edge: Work on the frontier of quantum computing and AI
- Impact: Solve real-world problems that classical ML cannot address efficiently
- Innovation: Freedom to explore new ideas and approaches
- Publication: Opportunities to publish research and attend ML/quantum conferences
- Growth: Learn from experts in both quantum physics and machine learning
- Flexibility: Remote-first with flexible hours
Projects You Might Work On
- Quantum Chemistry ML: Predict molecular properties using quantum neural networks
- Financial Optimization: Portfolio optimization with quantum approximate optimization
- Drug Discovery: Quantum-enhanced generative models for molecule design
- Logistics: Quantum-classical hybrid models for complex routing problems
- Materials Science: Predict material properties with quantum kernels
Team & Culture
You'll join a team of ~15 ML engineers, quantum physicists, and researchers who are passionate about pushing the boundaries of what's possible. We value:
- Curiosity: Asking "why" and "what if"
- Collaboration: Knowledge sharing and pair programming
- Excellence: High-quality code and rigorous validation
- Learning: Staying updated with latest research
- Impact: Focusing on real-world applications
Work Environment
- 100% Remote: Work from anywhere in the world
- Flexible Hours: Align with your productivity patterns
- Async Communication: Detailed documentation and async-first culture
- Team Meetings: Weekly team syncs and monthly all-hands
- Collaboration: Slack, GitHub, Notion for collaboration
- Equipment: Latest MacBook Pro or Linux workstation of your choice
Compensation & Benefits
- Salary: ₹18-30 LPA based on experience and location
- Equity: Meaningful equity stake in QuISTechAI
- Health: Comprehensive health, dental, and vision insurance
- Learning: ₹1,00,000 annual budget for courses, conferences, and books
- Equipment: Latest hardware and software tools
- Time Off: Unlimited PTO with 15 days minimum encouraged
- Parental Leave: 16 weeks paid parental leave
- Conferences: Attend NeurIPS, ICML, QIP, or other relevant conferences
Application Process
- Application Review (2-3 days): We review your resume and portfolio
- Initial Call (30 mins): Chat with our team about your background and interests
- Technical Interview (2 hours):
- ML fundamentals and quantum computing concepts
- Code a quantum ML algorithm (take-home or live)
- Team Interview (2 hours): Meet the ML and quantum teams
- Final Chat (45 mins): Discussion with leadership about vision and role
How to Apply
Send your application to careers@quistechai.com with:
- Resume/CV: Highlighting relevant experience
- Cover Letter: Why you're excited about quantum ML
- Portfolio:
- GitHub profile or code samples
- Links to papers/publications (if any)
- Blog posts or technical writing (if any)
- Optional: A quantum ML project you're proud of
No application deadline - we review applications on a rolling basis.
Questions?
Feel free to reach out to our recruiting team at careers@quistechai.com or connect with our team members on LinkedIn.
QuISTechAI is committed to building a diverse and inclusive team. We encourage applications from candidates of all backgrounds.