MLE 1 - Forecasting & Spatiotemporal Intelligence

Machine Learning Engineer 1 Forecasting & Spatiotemporal Intelligence

Location: Pune, India (On-site)

Type: Full-Time

Company Overview

Dispatch Network is building intelligent logistics models that learn and adapt in real time. Our systems combine forecasting, spatiotemporal modeling, and real-time optimization to make urban delivery networks faster, more reliable, and more efficient.

Role Overview

Were hiring a Machine Learning Engineer I to help develop and deploy the foundational forecasting and spatial intelligence models that power Dispatchs real-time fleet operations. You will work within the AI/ML team to build production-grade models using temporal and geospatial data.

Key Responsibilities:

Model Development

Implement forecasting and time-series models (LSTMs, Transformers, TCNs)

Contribute to spatial and spatiotemporal modeling using grid/H3-based systems or graph methods

Support feature engineering and data preparation for large-scale temporal and spatial datasets

Production ML Systems

Help build training pipelines for high-volume mobility and logistics data

Develop clean, production-ready Python code for training and inference

Assist in deploying real-time model endpoints and monitoring their performance

ML Ops & Evaluation

Run experiments and track results across multiple model iterations

Support model evaluation, baseline improvement, and error analysis

Work with senior engineers to implement monitoring and drift detection

Collaboration

Work closely with data engineering to ensure high-quality datasets

Coordinate with backend teams to integrate ML components into microservices

Participate in design discussions and contribute to documentation

Required Qualifications:

Experience

0.52 years of experience in ML engineering, or strong academic/internship projects

Exposure to time-series, forecasting, or geospatial modeling

Technical Skills

Strong foundation in machine learning and deep learning frameworks (PyTorch/TensorFlow)

Good understanding of temporal or spatial data processing

Proficiency in Python and familiarity with data engineering workflows

Basic understanding of model evaluation and experimentation practices

Soft Skills

Ability to learn quickly and work through ambiguity

Strong analytical skills and attention to detail

Clear communication and willingness to work across teams

Preferred Qualifications

Experience working with geospatial systems (H3, quadtrees, maps, mobility datasets)

Exposure to distributed data systems, ML pipelines, or feature stores

Prior work on forecasting models or mobility/logistics datasets

Experience contributing to production deployments