Machine Learning Engineer

The Mission Starts Here 

TheIncLab engineers and delivers intelligent digital applications and platforms that revolutionize how our customers and mission-critical teams achieve success.  

We are where innovation meets purpose; and where your career can meet purpose as well. We are looking for a Machine Learning Engineer that will focus on supporting the research, development, training, and evaluation of machine learning models used to solve complex, real-world problems.  We encourage you to apply and take the first step in joining our dynamic and impactful company.

This role is ideal for an engineer who has strong foundation in machine learning fundamentals and is eager to grow by working along senior ML engineers. The Machine Learning Engineer will contribute to model development, data preparation, experimentation, and evaluation while learning how to make informed architectural and modeling decisions.

This is not a role focused on integrating third-party AI services or prompt-based systems. The ideal candidate is interested in understanding how models work, how they are trained, and how data and design choices affect performance.

Your Mission, Should You Choose to Accept 

As a Machine Learning Engineer, you will join our Research & Product Innovation Department and team.   

What will you do? 

• Assist in researching and evaluating machine learning approaches under guidance
• Supervised, unsupervised, and learning
• Introductory reinforcement learning concepts
• Neural networks and classical ML techniques such as decision trees and ensemble methods
• Transformer-based models and Retrieval-Augmented Generation (RAG) systems
• Implement and train machine learning models using frameworks such as PyTorch, TensorFlow, or equivalent
• Support the formulation of ML-based solutions to optimization and decision-making problems
• Pathfinding and routing
• Basic combinatorial or constraint-based optimization
• Contribute to data pipelines for ML systems
• Data validation and quality checks
• Feature engineering and preprocessing
• Applying data augmentation techniques as directed
• Train, tune, evaluate models, identifying issues such as overfitting or underperformance
• Apply evaluation metrics to assess model performance and make interactive improvements with guidance
• For transformer-based systems: Assist with managing context windows and token budgets
• Implement chunking and retrieval strategies as directed
• Integrate trained models into existing systems with support from senior engineers
• Document experiments, results, and implementation details using tools such as Git, Jira, and Confluence
• Learn and follow best practices for ML experimentation, reproducibility, and software development
• Stay curious and engaged with emerging machine learning techniques and tools