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.