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 Senior Machine Learning Engineer to that will focus on researching, designing, training, and evaluating machine learning models to solve complex, real-world problems.  We encourage you to apply and take the first step in joining our dynamic and impactful company.

Your Mission, Should You Choose to Accept 

As a Machine Learning Engineer, you will research, evaluate, and select appropriate machine Learning approaches and architectures based on the problem definition.   

What will you do? 

• Research, evaluate, and select appropriate machine learning approaches and architectures based on the problem definition
• Supervised, unsupervised, and reinforcement learning
• Neural networks, decision trees, ensemble methods
• Transformer-based models, adversarial networks, genetic algorithms
• Retrieval-Augmented Generation (RAG) where appropriate
• Design and implement machine learning models using frameworks such as PyTorch, TensorFlow, or equivalent
• Formulate and solve optimization problems using ML techniques
• Pathfinding and routing
• Combinatorial and constraint-based optimization Heuristic and learning-based optimization approaches
• Own data pipelines for ML systems
• Data validation and quality checks
• Feature engineering and preprocessing
• Data augmentation strategies for training robustness
• Train, tune, and debug models, addressing issues such as overfitting, instability, bias, and performance degradation
• Define and apply appropriate evaluation metrics, analyze results and iteratively improve model performance
• For transformer-based systems
• Optimize context window usage Manage token budgets, chunking strategies, and retrieval mechanisms
• Balance performance, accuracy, and computational cost
• Integrate ML models and data pipelines into production systems
• Make technical decisions and provide architectural guidance for ML systems
• Document experiments, results, and design decisions using tools such as Git, Jira, and Confluence
• Mentor junior engineers and guide best practices in ML development Stay current with emerging ML research, tools, and techniques
• Ability to travel up to 20%