Machine Learning Engineer - Computational Software Development Who We AreNomad 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 fitforpurpose quantum sensors to allow us to see the world like never before. Who You AreYou are a voracious learner, a problem solver, and a doer. You are fascinated by emerging technologies and excited to help build a company with groundbreaking 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 worldleading sensors. You have an innate attention to detail and enjoy the challenge of modelling realworld systems. Role As a skilled computational machine learning engineer you will translate cuttingedge research and complex algorithms into robust, scalable, and productionready analytics software. You will work handinhand with the ML & Analytics Team and the Geophysics Team to develop endtoend geophysical modelling solutions. Responsibilities Collaborate with the ML & Analytics Team and geophysical subjectmatter 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 objectoriented 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 highquality, 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 dataintensive 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 realworld scientific or engineering problems. Excellent communication of complex ideas and problem solving within fastpaced team environments. History of thriving in diverse environments that value honesty, open communication, and strong bonds between team members. Australian Citizen or Permanent Resident. Nicetohaves Degree in Geophysics or experience in geophysical modelling/inversion techniques. Masters or PhD in quantitative methods. Familiarity with Bayesian statistics. Familiarity with Docker, AWS, and Linux. Benefits Fulltime role based in Melbourne, Australia. The role offers flexibility to work from home from time to time, but critical inperson 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 MidSenior level Employment type Fulltime Job function Other Industries IT Services and IT Consulting #J-18808-Ljbffr
Job Title
(Computational) Machine Learning Engineer