Job Opportunities API

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% Requirements Capabilities that will enable your success   Bachelor’s degree in Computer Science, Engineering, Applied Mathematics, or a related field 7+ years of professional experience, including significant hands-on machine learning development Strong understanding of machine learning theory and fundamentals Model selection and evaluation Bias/variance tradeoffs Optimization and loss functions Demonstrated experience training and evaluating models using frameworks such as PyTorch or TensorFlow Experience building and maintaining end-to-end ML pipelines Strong programming skills in Python (additional languages are a plus) Experience working with real-world, imperfect datasets Ability to explain model behavior, tradeoffs, and limitations to both technical and non-technical stakeholders Strong grasp of software engineering best practices and system design Preferred Qualifications   Experience with deep learning architectures (CNNs, RNNs, Transformers) Experience applying ML to optimization, planning, or decision-making problems Familiarity with distributed training or large-scale data processing Experience with experiment tracking tools (e.g., MLflow, Weights & Biases) Experience deploying ML models into production (batch or real-time inference) Background in research-driven or R&D-focused engineering environments Clearance Requirements   Applicants must be a U.S. Citizen and willing and eligible to obtain a U.S. Security