Senior Machine Learning Engineer

What You'll Do:

Build & Evolve Machine Learning Models
• Develop, improve, and maintain machine learning models that capture demand and supply dynamics within a digital manufacturing marketplace.
• Build and refine pricing-related models supporting intelligent and automated quoting.
• Develop models for cost estimation from CAD geometry, demand forecasting, and partner routing probability.
• Apply a range of machine learning techniques, including tree-based methods, probabilistic models, and deep learning, to solve both new and existing business challenges.
• Translate complex marketplace inputs such as part geometry, order history, and partner capacity into meaningful model features.
Design & Maintain ML Training and Inference Pipelines
• Design, build, and maintain reliable machine learning training and inference pipelines on AWS.
• Develop scalable workflows for experimentation, model training, validation, and deployment.
• Apply appropriate model versioning and experiment tracking practices.
• Build solutions that support reliable production machine learning systems.
• Contribute to scaling ML infrastructure as model and business requirements evolve.
Validate & Improve Model Performance
• Conduct offline experiments to evaluate and validate model improvements before deployment.
• Apply A/B testing and backtesting approaches to assess model performance.
• Monitor models in production and proactively identify model drift, degradation, or unexpected behaviour.
• Support monitoring, alerting, and retraining workflows to maintain model performance over time.
• Continuously improve models based on production performance and evolving business requirements.
Collaborate Across Engineering, Data & Product
• Work closely with ML Engineers, Data Scientists, Product teams, and domain experts in cross-functional teams.
• Collaborate with stakeholders to understand marketplace dynamics and translate business challenges into machine learning solutions.
• Work with real-world, complex, and imperfect datasets to develop practical solutions to ambiguous problems.
• Communicate model behaviour, results, and technical considerations effectively across technical and non-technical stakeholders.
• Contribute to the development of machine learning capabilities that support manufacturing intelligence and business growth.
Mentor & Support Engineering Talent
• Mentor and support mid-level and junior engineers within the team.
• Share machine learning engineering practices, technical knowledge, and lessons learned.
• Contribute to a collaborative engineering environment focused on continuous learning and improvement.
Stay Current with Machine Learning Innovation
• Stay up to date with advancements in machine learning, particularly in pricing, marketplace modelling, and manufacturing intelligence.
• Evaluate emerging approaches and techniques that could improve model performance and business outcomes.
• Apply relevant advances pragmatically to production machine learning challenges.

What It Takes:

Technical
• Proven experience building and deploying machine learning models in production environments.
• Strong coding skills in Python or a similar programming language.
• Hands-on experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn.
• Solid understanding of supervised and probabilistic modelling, including regression, classification, and uncertainty estimation.
• Experience with feature engineering from structured and/or geometric data.
• Hands-on experience with ML pipelines, model versioning, experiment tracking, and MLOps tools such as Weights & Biases, Prefect, Karpenter, or equivalent technologies.
• Experience designing and scaling ML infrastructure for production systems.
• Experience with ML monitoring, alerting, and retraining workflows.
Leadership & Collaboration
• Experience working effectively in cross-functional teams with ML Engineers, Data Scientists, Product teams, and domain experts.
• Strong communication skills, with the ability to explain complex machine learning models and concepts to non-technical stakeholders.
• Ability to work effectively with ambiguous problems and real-world, messy data.
• Experience mentoring and supporting junior and mid-level engineers.
• Ability to translate complex business and marketplace requirements into practical machine learning solutions.
Mindset
• Strong problem-solving mindset with the ability to work through ambiguous and complex challenges.
• Practical and data-driven approach to developing and improving machine learning solutions.
• Curiosity and willingness to explore emerging machine learning techniques and approaches.
• Strong ownership of model performance, reliability, and production outcomes.
• Collaborative mindset with a willingness to share knowledge and support the development of other engineers.
• Ability to balance experimentation and innovation with the reliability requirements of production systems.
Preferred Qualifications
• Experience with marketplace or pricing models, including demand modelling, price elasticity, or cost estimation.
• Background in operations research, econometrics, or supply chain optimisation.
• Experience working with 3D or geometric data, including CAD, point clouds, or mesh processing.
• Experience designing and scaling ML infrastructure for production systems.
• Experience with ML monitoring, alerting, and automated retraining workflows.
• Strong communication skills and experience explaining complex models to non-technical stakeholders.