Staff Engineer (R&D Engineering)

Category Engineering Hire Type Employee Job ID 17898 Remote Eligible No Date Posted 06/14/2026

Synopsys is the leader in engineering solutions from silicon to systems, enabling customers to rapidly innovate AI-powered products. We deliver industry-leading silicon design, IP, simulation and analysis solutions, and design services. We partner closely with our customers across a wide range of industries to maximize their R&D capability and productivity, powering innovation today that ignites the ingenuity of tomorrow. 

You Are

You have deep experience with computational physics and materials simulations, whether that comes from years in industry, a research-focused PhD, or postdoctoral work where you spent significant time implementing and running atomic-scale methods. You understand molecular dynamics, Monte Carlo techniques, or machine-learned potentials not just conceptually but practically, you have written the code, debugged the convergence problems, and analysed the results. 

Writing code in Python and C++ is something you do confidently. You may have built simulation workflows from scratch, contributed to research codebases, or optimized performance-critical sections of a larger platform. You know the difference between code that runs once for a paper and code that needs to be maintainable, and you care about that difference. Machine learning and AI techniques are tools you are ready to explore and apply, pushing simulations beyond what classical methods alone can achieve. 

You can communicate technical work clearly, whether that means writing a methods section for a journal paper, creating documentation that helps others reproduce your results, or explaining a complex simulation outcome to collaborators from different disciplines. At Synopsys, you will work on QuantumATK with a team that values both scientific rigor and the craft of building software that researchers and engineers depend on. 

What You'll Be Doing

• Implement and validate new simulation methods in QuantumATK, based on molecular dynamics, Monte Carlo, and machine-learned interatomic potentials 
  
• Develop algorithms that extend atomic-scale simulations toward longer timescales and continuum-level dynamics using machine learning and AI techniques 
  
• Write production-quality code in Python and C++ that integrates into the QuantumATK platform and meets performance, correctness, and maintainability standards 
  
• Prepare scientific case studies based on your implementations, co-author papers for peer-reviewed journals, and present results that demonstrate new capabilities 
  
• Create tutorials, example workflows, and documentation that help end users understand and apply new methods in their own research 
  
• Collaborate with physicists, chemists, and software engineers across the QuantumATK team to align method development with product roadmap and user needs 
  
• Test, benchmark, and optimize simulation workflows to ensure they scale and produce reliable results across different material systems and use cases 

The Impact You Will Have 

• Enable researchers and engineers to run simulations that were previously out of reach, bridging the gap between atomic-scale accuracy and the timescales needed for real materials problems 
  
• Accelerate the adoption of machine learning in materials modeling by building methods that are not just novel but actually usable in production workflows 
  
• Contribute to the scientific credibility of QuantumATK through peer-reviewed publications that showcase what the platform can do 
  
• Help users get more value from the platform by creating clear, practical tutorials that lower the barrier to advanced simulation techniques 
  
• Influence the direction of QuantumATK development by bringing deep domain expertise to discussions about what methods matter and how they should be implemented 
  
• Improve the performance and reliability of simulations that power semiconductor design, materials discovery, and next-generation device development 
  
• Strengthen the bridge between cutting-edge research and commercial software, ensuring that new science translates into tools people can depend on 

What You'll Need

• PhD in theoretical physics, computational chemistry, materials science, computer science, or equivalent research experience in atomic-scale simulations 
  
• Proven hands-on experience running and analysing atomic-scale simulations using methods like molecular dynamics, Monte Carlo, DFT, or related techniques 
  
• Strong programming skills in Python and C++, with a track record of writing code that others use or that has shipped in a research or production context 
  
• Solid grounding in condensed matter or polymer physics and chemistry, enough to evaluate whether a simulation result makes physical sense 
  
• Excellent written and spoken English, you have written scientific papers, documentation, or technical reports that communicate complex ideas clearly 
  
• Experience with machine learning or AI techniques applied to materials modeling or simulation workflows is a strong plus 
  
• Familiarity with QuantumATK or similar atomic-scale simulation platforms is a plus 

The Team You'll Be Part Of

You will join the QuantumATK development team in Copenhagen, a group of world-leading experts in atomic-scale simulations who build and maintain the QuantumATK platform. The team combines deep scientific expertise with serious software engineering, and your work will contribute directly to a product used by researchers and engineers across the semiconductor and materials industries. 

Rewards and Benefits 

We offer a comprehensive range of health, wellness, and financial benefits to cater to your needs. Our total rewards include both monetary and non-monetary offerings. Your recruiter will provide more details about the salary range and benefits during the hiring process.

At Synopsys, we want talented people of every background to feel valued and supported to do their best work. Synopsys considers all applicants for employment without regard to race, color, religion, national origin, gender, sexual orientation, age, military veteran status, or disability.