Reejig is seeking an experienced Senior Machine Learning Engineer to join our remote team based in Sydney. This is a fantastic opportunity to be part of a venture-backed, rapidly scaling startup where you’ll use cutting-edge technologies to solve fascinating problems and make a significant impact.

Responsibilities:

• Algorithm and Model Development: Develop and refine algorithms and models leveraging deep knowledge of data schema, quality, rationale, and domain expertise.
• Data Selection and Feature Engineering: Identify and select relevant data points, and perform feature engineering to optimise model performance.
• Model Setup and Tuning: Choose and set up appropriate algorithms, fine-tune parameters, and continuously evaluate and monitor their performance.
• End-to-End ML Pipeline: Design, implement, and maintain end-to-end production level machine learning pipelines on AWS, ensuring seamless integration and deployment.
• ML Models and LLMs: Utilise machine learning models and large language models (LLMs) to meet business needs, and develop effective prompts to enhance LLM interactions and outputs.
• Data Operations: Conduct bulk and incremental data operations to ensure data integrity and accessibility.
• Collaboration: Partner with cross-functional teams, including data scientists, engineers, and domain experts, to drive successful project outcomes.
• Continuous Learning: Stay updated with the latest advancements in machine learning and related fields to continuously improve skills and apply new techniques.

Requirements

The ideal candidate will have:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field.
• Minimum 5+ years of proven experience as an ML Engineer or similar role (e.g. Data Scientist or Data Engineer), with a focus on algorithm development and model building.
• Must have extensive hands-on experience in NLP techniques and tools (language modeling, text classification, named entity recognition) with a proven track record of applying these in real-world projects.
• High proficiency in Python, and experience with ML frameworks like Keras, TensorFlow, PyTorch, or Scikit-learn.
• Strong understanding of data schema, data quality, and data rationale, with the ability to work with large datasets and perform feature engineering.
• Experience in setting up and maintaining end-to-end machine learning pipelines, including model selection, training, tuning, and deployment.
• Proficient in utilising AWS cloud services for developing, deploying, and maintaining machine learning models and pipelines.
• Familiarity with large language models (LLMs) and prompt engineering techniques.
• Experience in performing bulk and incremental data operations.
• Strong analytical and problem-solving skills, with a detail-oriented mindset.
• Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams and a fast paced environment.
• A proactive approach to learning and applying new techniques to improve machine learning models and processes.