Responsibilities
- Assist in researching and evaluating machine learning approaches under guidance
- Supervised, unsupervised, and learning
- Introductory reinforcement learning concepts
- Neural networks and classical ML techniques such as decision trees and ensemble methods
- Transformer-based models and Retrieval-Augmented Generation (RAG) systems
- Implement and train machine learning models using frameworks such as PyTorch, TensorFlow, or equivalent
- Support the formulation of ML-based solutions to optimization and decision-making problems
- Pathfinding and routing
- Basic combinatorial or constraint-based optimization
- Contribute to data pipelines for ML systems
- Data validation and quality checks
- Feature engineering and preprocessing
- Applying data augmentation techniques as directed
- Train, tune, evaluate models, identifying issues such as overfitting or underperformance
- Apply evaluation metrics to assess model performance and make interactive improvements with guidance
- For transformer-based systems: Assist with managing context windows and token budgets
- Implement chunking and retrieval strategies as directed
- Integrate trained models into existing systems with support from senior engineers
- Document experiments, results, and implementation details using tools such as Git, Jira, and Confluence
- Learn and follow best practices for ML experimentation, reproducibility, and software development
- Stay curious and engaged with emerging machine learning techniques and tools
- Bachelor’s degree in Computer Science, Engineering, Applied Mathematics, or a related field
- 1-3 years of professional experience or equivalent academic/project experience in machine learning or data science
- Strong understanding of core machine learning concepts to include basic model selection, evaluation, overfitting, generalization, loss functions, and optimization fundamentals
- Hands-on experience training models using frameworks such as PyTorch or TensorFlow
- Proficiency in Python
- Experience working with real-world datasets, including cleaning and preprocessing
- Ability to learn quickly and apply feedback from senior engineers
- Strong problem-solving skills and attention to detail
- Ability to travel up to 20%
Preferred qualifications
- Internship, research, or project experience involving machine learning model training
- Exposure to deep learning architectures such as CNNs or Transformers
- Familiarity with experiment tracking or visualization tools
- Experience deploying models in academic, prototype, or production-like environments
- Interest in optimization, planning or decision-making problems
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
About the company
- Salary range guidance provided is not a guarantee of compensation. Offers of employment may be at a salary range that is outside of this range and will be based on qualifications, experience, and possible contractual requirements.
- **This is a direct hire position, and we do not accept resumes from third-party recruiters or agencies.
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