What are the responsibilities and job description for the AI/ML Lead Engineer position at Programmers.io?
Location - NJ/TX/AZ /RI
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 Replica’s models.
- Develop new algorithms to fuse Replica’s 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 Replica’s challenges.
Minimum Qualifications
- Bachelor’s 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.
