Software Engineer

Job Description

Job Description

About the Role

We’re building a next-generation Fleet Operations Platform to power large-scale vehicle networks: integrating ride-share systems, IoT telemetry, real-time data pipelines, optimization engines, and AI-native workflows.

This role is heavily focused on backend system design and large-scale distributed architecture, not just building CRUD APIs.

You'll work on:

• High-throughput telematics ingestion (GPS, sensors, diagnostics)

• Distributed, event-driven systems at scale

• Optimization engines for dispatch, routing, and revenue

• AI-assisted engineering workflows and AI-powered platform capabilities

• Real-time decision systems that combine telemetry, analytics, and machine intelligence

Responsibilities

• Design and build scalable backend services for fleet operations

• Architect event-driven systems (Kafka / Pulsar)

• Develop real-time data pipelines (IoT + telemetry ingestion)

• Implement optimization algorithms (routing, scheduling, matching)

• Work closely with Data/ML engineers on prediction, optimization, and AI-driven systems

• Leverage AI tools such as Claude, Codex, Gemini, Cursor, and similar systems to improve engineering velocity and software quality

• Evaluate, validate, and review AI-generated code, designs, and recommendations with sound engineering judgment

• Own services end-to-end (design → build → deploy → operate)

• Integrate external APIs (rideshare networks, telematics providers)

• Ensure high reliability, observability, security, and performance

Technical Stack

Backend & Core Systems

• Kotlin / Java (Micronaut)

• Python (for ML services, data processing, optimization, and AI workloads)

Data & Streaming

• PostgreSQL, Redis

• Kafka or Pulsar

• Flink / Spark (stream + batch processing)

Infrastructure

• AWS (EKS, S3, RDS, etc.)

• Kubernetes (K8s)

• Docker

• Infrastructure as Code (Terraform is a plus)

Engineering Productivity

• Claude

• Codex

• Gemini

• Cursor

• Modern AI-assisted development workflows

What We're Looking For

Core Requirements

• 2+ years building backend or distributed systems

• Strong experience with:

  • Event-driven architecture

  • High-throughput / real-time systems

  • API design & integrations

• Comfortable using modern AI engineering tools to accelerate development while maintaining high quality standards

Problem Solving & Algorithms

You should be strong in:

• Data structures & algorithms

• System design & tradeoffs

• Evaluating and validating AI-generated solutions

Bonus if you have experience with:

• Graph algorithms (routing, shortest path)

• Scheduling / matching systems

• Optimization problems (greedy, DP, heuristics)

Systems Thinking

Deep understanding of:

• Consistency vs availability

• Latency vs throughput

• Horizontal scaling

• Failure modes in distributed systems

• Tradeoffs between human-written and AI-generated solutions

• Ability to design systems from 0 → 1 and scale

AI-Native Engineering Mindset

We are an AI-native engineering organization.

We expect engineers to:

• Use AI tools daily to accelerate development, debugging, testing, documentation, and system design

• Critically evaluate AI outputs rather than blindly accepting generated code

• Validate solutions through testing, observability, benchmarking, and code review

• Understand the limitations, risks, and tradeoffs of AI-generated implementations

• Maintain strong engineering fundamentals independent of AI assistance

Nice to Have

• Experience with IoT / telemetry systems

• Background in rideshare, logistics, or mobility

• Exposure to ML systems, LLM systems, or data pipelines

• Experience with Flink or real-time stream processing

• Experience deploying systems on Kubernetes (EKS)

• Experience building AI-powered products or internal AI tooling

What Makes This Role Different

• Real-world high-scale distributed systems

• Strong focus on algorithms + optimization

• AI is a core part of how we design, build, and operate software

• Opportunity to shape architecture from an early stage

• Work with a top-tier, high-bar engineering team

• Build alongside engineers who effectively leverage AI without sacrificing engineering rigor

Compensation

• Competitive (based on location & experience)

• High ownership and impact

• Long-term growth with a scaling platform

How to Stand Out

• Show systems you've built (not just features)

• Demonstrate strong problem-solving ability

• Highlight experience with:

  • Distributed systems at scale

  • Optimization / algorithmic problems

  • Real-time data pipelines

  • Effective use of AI tools in production engineering workflows

• Share examples where you identified issues in AI-generated code or used AI to significantly improve engineering productivity