AI ML

Responsibilities

• Design prototype and implement applied AI/ML models for mobility activity and behavioral modeling at scale.
• Drive improvements to core models for routing mode choice demand estimation and geospatial forecasting - balancing research innovation with production readiness.
• Partner closely with Product and Data Science leadership to set technical direction for modeling initiatives ensuring they align with customer and business needs.
• Evaluate new data sources (e.g. vendor-provided mobility data traffic sensors geospatial layers) for quality coverage and fitness-for-use in Replicas models.
• Develop new algorithms to fuse Replicas proprietary data with external sources (e.g. traffic sensors land use census streaming data).
• Evaluate model performance define success metrics and iterate quickly to improve accuracy robustness and fairness.
• Collaborate with data production engineers to transition prototypes into robust production-quality systems used nationwide.
• Contribute to modeling frameworks and shared libraries mentoring peers and raising the technical bar across the team.
• Stay current with advances in machine learning optimization and geospatial modeling applying them pragmatically to Replicas challenges.

Minimum Qualifications

• Bachelors degree in Computer Science Applied Mathematics Engineering or related field
• 4 years of professional experience in applied ML/AI research engineering or related roles.
• Strong Python skills with experience in ML libraries (PyTorch TensorFlow scikit-learn) and numerical computing (numpy pandas scipy).
• Experience designing training and deploying AI/ML models in production.
• Hands-on experience with large-scale data sets especially geospatial or time-series data.
• Familiarity with distributed computation frameworks (Dask Spark Ray) and cloud environments (GCP preferred).
• Proven ability to communicate technical decisions and tradeoffs clearly to both technical and non-technical stakeholders.