Black Canyon Consulting
AI/ML Engineer
About this role
Black Canyon Consulting seeks an AI/ML Engineer to design, develop, and deploy machine learning systems that solve complex business challenges. You'll collaborate with cross-functional teams to transform data science prototypes into production-ready solutions and stay current with emerging AI technologies.
What you'll do
- Design, develop, and implement machine learning systems and applications
- Create, train, evaluate, and fine-tune machine learning models based on performance results
- Research and implement appropriate ML algorithms, frameworks, and tools
- Transform data science prototypes into scalable, production-ready solutions
- Conduct ML tests and experiments; perform statistical analysis to optimize model performance
- Collaborate with stakeholders to identify business and technical opportunities for AI-enabled solutions
What they're looking for
- Machine learning algorithms and techniques
- Python, Java, and/or R development
- ML frameworks (PyTorch, Keras)
- ML libraries (scikit-learn)
- Statistics, probability, and mathematics
- Data modeling and software architecture
- Statistical analysis and model evaluation
- Data structures and dataset selection
Benefits
- Medical, dental, and vision coverage
- 401(k) plan with employer contribution
- Paid holidays and vacation
- Tuition reimbursement
- Competitive benefits package
- High-performing, professional organization
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Black Canyon Consulting
Black Canyon Consulting provides security, infrastructure, and modernization services for high-traffic biomedical research platforms, particularly supporting the National Center for Biotechnology Information (NCBI) at the National Institutes of Health. The company is hiring for security engineers, DevSecOps specialists, full-stack developers, QA engineers, and platform engineers to build resilient, compliant systems serving millions of users across hybrid cloud and on-premises environments.
View all jobs at Black Canyon ConsultingLikely interview questions
- Walk us through a machine learning project where you transformed a prototype into a production-ready solution—what challenges did you encounter?
- How do you approach selecting appropriate datasets and data representation methods for a given ML problem?