MLE - Forecasting & Spatiotemporal Intelligence

Position: 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

We’re hiring a Machine Learning Engineer I to help develop and deploy the foundational forecasting and spatial intelligence models that power Dispatch’s 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

• 0.5–2 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/Tensor Flow)

• 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