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%
