(Computational) Machine Learning Engineer

Machine Learning Engineer - Computational Software Development Who We Are Nomad Atomics is on a mission to make the broad uptake of quantum sensing a reality and simultaneously push the limits of our field beyond what we think is possible. We are building the world's most advanced fit‑for‑purpose quantum sensors to allow us to see the world like never before. Who You Are You are a voracious learner, a problem solver, and a doer. You are fascinated by emerging technologies and excited to help build a company with ground‑breaking ideas. You are excited to operate at the forefront of technology development and be the first in the world to demonstrate the true capability of these world‑leading sensors. You have an innate attention to detail and enjoy the challenge of modelling real‑world systems. Role As a skilled computational machine learning engineer you will translate cutting‑edge research and complex algorithms into robust, scalable, and production‑ready analytics software. You will work hand‑in‑hand with the ML & Analytics Team and the Geophysics Team to develop end‑to‑end geophysical modelling solutions. Responsibilities Collaborate with the ML & Analytics Team and geophysical subject‑matter experts to build, test, and maintain Nomad computational software and quantum gravity sensor data processing pipelines. Take novel computational techniques and algorithm prototypes designed by our research team and engineer them into reliable, performant software modules. Build robust data ETL pipelines and software scaffolding. Develop comprehensive unit and integration tests to ensure scientific accuracy and reliability of the codebase. Contribute to DevOps and MLOps practices, including containerisation (Docker), CI/CD pipelines, and future deployments on cloud platforms (AWS). Work with technology and deployment experts to build the software tools needed for highly efficient surveying techniques. Requirements Exceptional machine learning/computational software development skills in Python (required). Strong background in DevOps and MLOps, including version control (Git), CI/CD, API design, unit testing, data and experiment tracking, and object‑oriented programming (required). Strong quantitative intuition and proven ability to translate complex mathematical concepts from domains such as machine learning, signal processing, and statistical simulation into high‑quality, efficient code (required). Demonstrated experience with Python libraries: SciPy, NumPy, JAX, PyTorch, TensorFlow, Matplotlib, Plotly, PyMC, multiprocessing. 3+ years of professional experience in a computational software development or data‑intensive role, OR a portfolio of personal projects that demonstrates an equivalent level of skill and a passion for building complex scientific software. A degree in a quantitative field such as Computer Science, Engineering, Physics, or Mathematics. Experience applying machine learning techniques to solve real‑world scientific or engineering problems. Excellent communication of complex ideas and problem solving within fast‑paced team environments. History of thriving in diverse environments that value honesty, open communication, and strong bonds between team members. Australian Citizen or Permanent Resident. Nice‑to‑haves Degree in Geophysics or experience in geophysical modelling/inversion techniques. Master’s or PhD in quantitative methods. Familiarity with Bayesian statistics. Familiarity with Docker, AWS, and Linux. Benefits Full‑time role based in Melbourne, Australia . The role offers flexibility to work from home from time to time, but critical in‑person interaction with our tech and geophysical teams will be essential. We offer competitive salary, employee share option package, and opportunities for professional growth and advancement. Seniority level Mid‑Senior level Employment type Full‑time Job function Other Industries IT Services and IT Consulting #J-18808-Ljbffr