Senior Machine Learning Engineer

TheIncLab • McLean, VA

4h • Hybrid

About The Position

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.

Requirements

• 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
• Applicants must be a U.S. Citizen and willing and eligible to obtain a U.S. Security Clearance at the Secret or Top-Secret level. Existing clearance is preferred.

Nice To Haves

• 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

Responsibilities

• 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%

Benefits

• Hybrid and flexible work schedules
• Professional development programs
• Training and certification reimbursement e options for Me
• Extended and floating holiday schedule
• Paid time off and Paid volunteer time
• Health and Wellness Benefits includdical, Dental, and Vision insurance along with access to Wellness, Mental Health, and Employee Assistance Programs.
• 100% Company Paid Benefits that include STD, LTD, and Basic Life insurance.
• 401(k) Plan Options with employer matching Incentive bonuses for eligible clearances, performance, and employee referrals.
• A company culture that values your individual strengths, career goals, and contributions to the team

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 1-10 employees