MLE – Spatiotemporal Intelligence

Position: MLE 2 – Spatiotemporal Intelligence
MLE 2 - Spatiotemporal Intelligence

Location:

Pune, India (On-site)

Type:
Full-Time

Company Overview

Dispatch Network is building the most efficient last-mile network in India from the ground up using technology and AI-powered optimization to drive efficiency and earnings for delivery partners. We operate across food delivery, quick commerce, grocery, ecommerce, and pharma.

Dispatch Network is building an adaptive logistics intelligence platform — a system that learns city dynamics in real time and optimizes how goods move through urban space. We’re moving from pilot to national scale, and the AI layer you build here will shape how fleets behave across India’s densest delivery environments.

Role Overview

We’re hiring a Machine Learning Engineer II to independently design, build, and deploy spatiotemporal and forecasting models s role owns significant model pipelines and works across engineering to build reliable, real-time ML systems for complex logistics environments.

Key Responsibilities

Model Architecture & Development

• Build and own forecasting and spatiotemporal models (Transformers, diffusion forecasters, GNNs)

• Develop geospatial intelligence using H3 indexing, mobility modeling, or graph-based systems

• Design features and pipelines for large temporal and spatial datasets

Production ML Systems

• Build scalable training pipelines, feature engineering flows, and inference services

• Deploy low-latency model endpoints supporting real-time fleet decisions

• Implement model retraining workflows, drift detection, and automated performance monitoring

MLOps & Governance

• Establish experiment tracking, model versioning, and performance tracking practices

• Conduct deep error analysis and optimize models for reliability and stability

• Define evaluation metrics aligned with operational constraints: SLA accuracy, idle km, throughput

Technical Leadership

• Mentor MLE I engineers and guide best practices in modeling and pipelines

• Work closely with backend teams to integrate ML systems into microservices

• Contribute to architecture decisions, documentation, and cross-functional planning

Required Qualifications

Experience

• 2–5 years of experience building and deploying ML models in production

• Strong experience with time-series forecasting, spatial modeling, or mobility datasets

Technical Skills

• Proficiency with PyTorch or Tensor Flow and production-grade Python

• Experience with distributed data systems, training pipelines, and ML orchestration

• Strong understanding of model deployment, inference optimization, and monitoring

• Familiarity with geospatial models, H3/hex grids, or GNN-based architectures

Soft Skills

• Ability to break down ambiguous problems and own end-to-end systems

• Strong communication with engineering, product, and operations teams

• Ability to mentor junior ML engineers

Preferred Qualifications

• Experience with real-time inference or high-throughput ML systems

• Background in operations research, applied math, or geospatial analytics

• Work with logistics, mobility, or high-frequency forecasting problems

• Open-source contributions to ML or data infrastructure