Senior Machine Learning Engineer
New
This role is remote-friendly within AustraliaFull-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- PythonMachine LearningPyTorchTensorflowPrompt EngineeringGenerative AI
Requirements
- Highly skilled machine learning engineer with a proven track record of delivering ML-powered product features in production
- Deep technical expertise across the ML lifecycle - from data wrangling and model development to evaluation, deployment, and monitoring
- Comfortable operating independently while collaborating deeply with cross-functional teams
- Motivated by user impact and product outcomes
- Demonstrated experience building and deploying ML-powered features in production environments
- Proficiency with Python and frameworks like TensorFlow or PyTorch
- Skilled across the ML lifecycle: data processing, model training, evaluation, deployment, and monitoring
- Proven ability to work independently while collaborating cross-functionally with product, design, and data
- Experience with Generative AI (GenAI): LLMs, diffusion models, prompt engineering, RAG, or fine-tuning
- Familiarity with GenAI platforms (e.g. OpenAI, Hugging Face, Anthropic) and embedding-based systems
- Strong product mindset - prioritises ML solutions that improve user experience and product outcomes
- Committed to scalable, maintainable ML systems with clear metrics and impact tracking
- Passionate about ethical ML and responsible data usage, especially in learning environments
- Enjoys mentoring and sharing best practices within the team
Responsibilities
- Building, testing, and shipping ML-powered features that enhance our ad generation experiences
- Designing scalable ML pipelines in collaboration with our platform teams
- Working closely with PMs, designers and engineers to align on user needs and product direction
- Running experiments to validate assumptions and improve model effectiveness
- Contributing to shared ML infrastructure and best practices at Canva
- Keeping up with state-of-the-art developments and exploring ways to apply them at scale
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