Senior Scientific Software Engineer — Simulation and Machine Learning

constructorknowledgelabs; United States; Hybrid; USD 104,000 - 173,000; Full time

Benefits offered by this job

Hybrid remote work

Teaching opportunities

Supervision of students

Job summary

Constructorknowledgelabs is seeking a senior engineer to build the simulation and model-learning engine at the core of its quantum software platform. The role blends physics and engineering, combining differentiable simulation with ML-driven parameter inference.

You will develop high-performance code for open and closed quantum systems, own the differentiable simulation and AD layer, and drive performance in optimisation loops.

Qualifications

• PhD or equivalent in physics, applied mathematics, or computational science.
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Responsibilities

• Build and maintain high-performance simulation code for open and closed quantum systems.

Skills

Numerical computing

Julia/Python/JAX

ODE solvers

Automatic differentiation

Technical writing

English fluency

Research publication

Education

PhD or equivalent in physics/applied math/computational science

Tools

Julia

Python

JAX

Job description

Role overview

This senior engineering position focuses on building the simulation and model-learning engine at the core of a quantum software platform. The work sits at the intersection of physics and engineering, combining differentiable simulation of physical systems with machine-learning-driven parameter inference and large-scale numerical optimisation. It is a research-grade engineering role with a clear delivery mandate rather than a pure research post or a general software position.

Responsibilities

• Build and maintain high-performance simulation code for open and closed quantum systems
• Own the differentiable simulation and automatic differentiation layer, and drive performance inside optimisation loops
• Implement parameter inference and model learning from experimental data
• Develop neural-network approaches to PDE solving and equation discovery
• Benchmark against established academic tools and publish the comparisons
• Collaborate with faculty on joint research and grant proposals, supervise graduate students and interns, and contribute to teaching material on numerical simulation and ML for physics

Requirements

• PhD or equivalent in physics, applied mathematics, or computational science
• Strong scientific computing background in Julia, Python, JAX, or comparable environments
• Genuine numerical depth in ODE solvers, optimisation, stochastic processes, and automatic differentiation
• Published research or widely used code that others rely on
• Ability to take a method from a paper and turn it into fast, tested, usable software
• Clear technical writing and fluent English

Nice to have

• Interest in supervising students alongside engineering work
• Comfort owning code quality, not just correctness
• Motivation to contribute to teaching and curriculum development

Benefits and work setup

• Remote within Europe with regular on-site presence in Bremen
• Hybrid working model combining remote flexibility with time on-site
• Route into teaching and student supervision for those who want it
• Direct involvement in core technical work with measurable impact on performance and accuracy