Machine Learning Ops Engineer

We are looking for a high-agency Machine Learning Ops Engineer to join our Infrastructure Team. You will help build and maintain the platform used to train, evaluate, and serve our AI models to clients in the mining industry. Your work will directly support our Technical Services and Platform teams in delivering solutions that create value for mining clients. This position requires strong expertise in Python and machine learning workflows. You will work alongside a team of three engineers focused on creating robust infrastructure and tooling. This is a remote-first position based in Canada. Key Responsibilities Develop robust and well-tested code for core internal tools: Create data preprocessing modules for mining data Implement metrics calculations and evaluation pipelines Build visualization tools for 3D models and ML performance metrics Troubleshoot and fix issues in existing metrics code Build and maintain our custom end-to-end MLOps platform: Implement experiment tracking systems Create model registry with versioning and storage Develop automated testing frameworks Build interfaces between different components of the ML pipeline Develop production-grade QA/QC systems for deployed AI models: Implement input data validation Create automated alerts for performance issues Set up monitoring for data drift Build dashboards for model performance metrics Create specialized tools for mining data: Implement spatial data processing utilities Build visualization tools for 3D geological data Develop data converters between different mining data formats Create utilities for coordinate transformations Refactor and productionize code created by the client services team: Convert notebooks into modular Python packages Implement proper error handling and logging Add comprehensive testing to existing code Improve performance of data processing pipelines Provide technical expertise to the client services team Manage infrastructure for data processing, model training, and serving Mentor junior engineers, perform code reviews, and write documentation Proactively identify technical challenges and drive improvement initiatives Technical Competencies & Requirements Bachelor's degree in Computer Science, Engineering, or related fields OR equivalent experience in software development and ML engineering 3+ years of industry experience Kubernetes, PyTorch Advanced Python programming skills: Proficiency with data science libraries (numpy, pandas) Experience with visualization tools Ability to write modular, robust, and tested Python code Strong debugging skills for complex ML systems Deep learning experience: Implementation of neural network models and training workflows Understanding of model architecture selection Knowledge of model evaluation techniques MLOps expertise: Creating experiment tracking systems Building model registries and versioning systems Implementing model deployment pipelines Setting up monitoring for model performance Data engineering capabilities: Experience with SQL and database principles Familiarity with database frameworks Ability to create data processing pipelines Experience handling common mining data formats and transformations Infrastructure management: Experience with cloud services (AWS/Azure) Understanding of containerization (Docker or Singularity) Knowledge of compute resources for ML Testing and quality assurance: Implementing automated tests for ML systems Creating QA/QC systems for model predictions Designing validation steps for data inputs/outputs Ability to write efficient software following best practices Proven ability to thrive in startup environments with low structure and high autonomy Strong technical communication skills and ability to collaborate in a remote team setting Experience working with machine learning in computer vision, NLP, recommender systems, or scientific applications Strong background in probability, machine learning, and data science Strong experience with data analysis/processing libraries such as pandas and numpy Excellent communication skills for both technical and non-technical audiences Self-learner and motivated to pick up new skills Nice to Have Previous experience working at startups Familiarity with Git, experiment tracking tools (WandB, Comet, etc.) Experience working on production machine learning using tools such as KubeFlow, MLFlow, AirFlow, Seldon Core, DVC, Spark, etc. Written/oral fluency in a language besides English Experience optimizing data processing pipelines and/or neural network models Proficiency in a lower-level programming language or GPU programming Experience with data application frameworks Full stack development experience Experience with experiment tracking systems and ML model monitoring Background in mining or resource modeling About Stratum We're Stratum, a mining software company with machine learning models as our core product. Our 3D maps predict how gold, silver, copper, etc. are distributed (and how much!) using only small amounts of data, unconventional data processing, and proprietary ML protocols. Our work directly affects how much money a mine is going to make next week/month/year while reducing waste/cost. We're supported by Founders Fund, Aramco, Builders VC, Y Combinator, and Ilya Sutskever, former Chief Scientist at OpenAI, who have recognized the potential of our industry-disrupting technology. Our long-term vision is to build a massive AI engine capable of making every decision in a mining operation, down to moving individual rocks. If you’re an exceptional engineer interested to helping make this vision a reality we look forward to reviewing your application and working together.

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