ML Engineer.

Machine Learning Engineer

Sydney (Hybrid)

$150-170k

We are working with an extremely exciting fast paced SaaS company that provides cutting-edge software solutions to businesses around the world. The platform leverages the power of data and machine learning to deliver intelligent, scalable, and impactful solutions for our clients. 

You will be responsible for designing, developing, and deploying machine learning models that solve real-world problems and enhance the functionality of our software products. You will collaborate with cross-functional teams including product management, data science, and software engineering to build intelligent solutions that provide value to our customers.

Key Responsibilities:

• Design, implement, and deploy scalable machine learning models and algorithms to enhance our SaaS platform.
• Work with large datasets to train, validate, and optimize models.
• Collaborate with data engineers to build and maintain data pipelines and ensure the integrity and quality of training data.
• Implement A/B testing frameworks to measure the performance of models in production.
• Monitor, evaluate, and continuously improve the performance of deployed models.
• Stay up-to-date with the latest advancements in machine learning, deep learning, and related fields, and apply these insights to our product offerings.
• Integrate machine learning models into cloud-based software solutions (e.g., AWS, GCP, or Azure).

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.
• 3+ years of experience as a Machine Learning Engineer or in a related role.
• Proficiency in Python and popular machine learning libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Experience with data processing and analysis using tools like Pandas, NumPy, or similar.
• Strong understanding of cloud-based infrastructure (AWS, Google Cloud, or Azure) and experience in deploying machine learning models in a cloud environment.
• Experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, Airflow).
• Familiarity with containerization (e.g., Docker) and orchestration tools (e.g., Kubernetes).
• Strong problem-solving skills and a passion for working with data.

Nice-to-Have:

• Experience with natural language processing (NLP) or computer vision.
• Familiarity with big data tools such as Spark or Hadoop.
• Experience working in an agile development environment.
• Knowledge of SaaS development practices and methodologies.