Develop end-to-end machine-learning models for autonomous-driving taxis from camera and LiDAR data, then evaluate and optimize them in simulation and real vehicles.
About this role
Newmo is hiring a Senior Product Engineer to lead end-to-end product development for its taxi business, covering requirements, specifications, implementation, improvement, quality assurance, and rollout to field operations. The role addresses operational issues arising across an approximately 1,500-vehicle company taxi fleet and works with product managers, business teams, and frontline operations to turn feedback into product improvements.
The work includes identifying operational problems, defining requirements and specifications, and improving performance against utilization rate, acceptance rate, and passenger wait time. The engineer will help redesign taxi operations through software, including vehicle management, remote monitoring, maintenance, cleaning, and charging control. The role also covers real-time systems for roll calls, dispatching, driving operations, and internal staff work, as well as replacing Excel and paper-based processes and reducing frontline workload through functions such as alcohol checks.
Product development runs from understanding field operations through modeling, specification, implementation, QA, and migration into field use. The role includes designing and advancing a phased migration from existing dispatch and operations-management systems. Core products include passenger dispatch applications, driver applications, roll-call systems, dispatch-room consoles, AI dispatch systems, and operational platforms. Examples of product work include payment and destination-unspecified dispatch for passengers, pickup functions, navigation, and meter integration for drivers, and face-to-face roll calls, automated post-operation roll calls, and alcohol checks for operations teams.
The role also contributes to the design and improvement of an integrated operations platform covering shifts, vehicle assignments, roll-call schedules, attendance, and maintenance schedules. It includes developing dispatch-matching logic to optimize utilization, acceptance rate, and wait time, while continuously improving quality, security, and technical debt for a regulated industry. This operational foundation is intended to support future autonomous taxi infrastructure, including depot operations, remote supervision, maintenance, cleaning, and charging control.
The named development environment includes Go, Python, TypeScript, Kotlin, and Swift; a Modular Monolith architecture; GraphQL and gRPC with Protocol Buffers; AlloyDB with PostgreSQL compatibility and GIS; BigQuery with dbt; Google Cloud, Cloudflare, and some AWS; Terraform, Atlas, and Bytebase; Google Maps Platform and Google Fleet Engine; GitHub Actions, OpenTelemetry, Datadog, and Lightdash; and Slack and Google Workspace. AI tools named in the posting include Claude, Devin, GitHub Copilot, and Google Gemini. The work location is in Toranomon, Minato-ku, Tokyo.
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