TheIncLab

Senior Machine Learning Engineer

TheIncLab

full-time

Posted on:

Location Type: Hybrid

Location: McLean • Tennessee, Virginia • 🇺🇸 United States

Visit company website
✨ AI Apply
Apply

Job Level

Senior

Tech Stack

PythonPyTorchTensorflow

About the role

  • 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
  • Optimize context window usage for transformer-based systems
  • 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

  • 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
  • Experience with deep learning architectures (CNNs, RNNs, Transformers) (Preferred)
  • Experience applying ML to optimization, planning, or decision-making problems (Preferred)
  • Familiarity with distributed training or large-scale data processing (Preferred)
  • Experience with experiment tracking tools (e.g., MLflow, Weights & Biases) (Preferred)
  • Experience deploying ML models into production (batch or real-time inference) (Preferred)
Benefits
  • Hybrid and flexible work schedules
  • Professional development programs
  • Training and certification reimbursement options for employees
  • Extended and floating holiday schedule
  • Paid time off and Paid volunteer time
  • Health and Wellness Benefits including Medical, 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

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills
machine learningsupervised learningunsupervised learningreinforcement learningneural networksdecision treesensemble methodsfeature engineeringdata validationoptimization
Soft skills
mentoringcommunicationproblem-solvingtechnical decision-makingcollaborationexplanation of model behaviorguidance on best practicesadaptabilityanalytical thinkingattention to detail
Certifications
Bachelor's degree in Computer ScienceBachelor's degree in EngineeringBachelor's degree in Applied Mathematics