Top Tech Jobs & Startup Jobs

Develop and ship end-to-end machine learning models for autonomous driving behaviors across L2-L4 systems. Own data curation, training, evaluation, experimentation, and on-road validation. Improve model performance through iterative training cycles while addressing real-world safety, quality, and deployment constraints. The role may involve reinforcement learning, transformer networks, automotive systems, model compression, and embedded deployment.

Own the full AI model release lifecycle, from training and evaluation through on-road testing and fleet deployment. Define promotion gates, manage regressions and rollbacks, improve release processes, build dashboards and KPIs, influence automated tooling, and coordinate ML engineering, data science, software platform, testing, and product teams. Serve as the central contact for release status, risks, timelines, and continuous improvement.

Manage Japan-based People Operations across the employee lifecycle, including onboarding, offboarding, HRIS administration, attendance, payroll coordination, tax, social and labour insurance, benefits, health checks, documentation, and compliance. Partner with employees, managers, external providers, and internal teams while improving processes and systems. The role requires fluent Japanese and working English and is based in a hybrid Tokyo office.

As a Staff Machine Learning Engineer, you will lead the development and deployment of AI models for autonomous driving, focusing on safety and reliability. Responsibilities include driving the roadmap, training models, and collaborating across teams.

Lead reinforcement learning development for end-to-end autonomous driving models. Responsibilities include designing offline and off-policy RL methods, improving reward models, building large-scale training and evaluation workflows, diagnosing distribution shift and optimization failures, validating policies through simulation and on-road testing, productionizing successful methods, and mentoring engineers. The role also leads development of learned emergency trajectory models for safety-critical maneuvers such as evasive steering and emergency braking.

Own robotaxi programs from vehicle and fleet readiness through launch and in-market scaling in Japan. Coordinate autonomy, vehicle software and hardware, operations, safety, engineering, and local partners. Manage dependencies, risks, delivery rhythms, stakeholder communications, metrics, and continuous improvement while supporting on-the-ground operations and local vehicle programs in a safety-critical, cross-cultural environment.

Own defined portions of Robotaxi system architecture, translating use cases into requirements, interfaces, specifications, and verification plans. Collaborate with OEM, Tier1, and operator partners to establish technical boundaries and lead design discussions. Apply architectures across vehicle platforms, support integration and bring-up, triage issues, assess design changes, and address safety, redundancy, degraded operation, and offboard service considerations for production-intent L4 autonomous systems.

Build and operate secure, reliable, and cost-effective AI platform infrastructure for language models and agentic workflows. Responsibilities include developing runtimes, services, developer tooling, observability, governance controls, deployment automation, and reusable platform capabilities. Partner with Security, IT, and engineering teams to integrate and scale the platform, support adoption, document best practices, and troubleshoot issues. The role also owns projects end to end and participates in on-call support.

Designs and evolves scalable data pipeline, orchestration, and inference infrastructure for autonomous driving data platforms. Responsibilities include developing DAG-based workflow systems, tooling, shared libraries, batch inference capabilities, and high-throughput processing workflows. The role focuses on reliability, performance optimization, resource prioritization, incident response, and collaboration with partner teams to support large-scale data ingestion, processing, evaluation, and machine learning operations.

Develop and maintain Wayve’s embedded Linux software stack for autonomous vehicle fleets. Responsibilities include Linux kernel and device-driver development, YoCTO-based distribution maintenance, high-performance C/C++ software development, diagnostics, fault tolerance, debugging, and secure fleet hardening. The role collaborates across engineering, product, hardware, supply chain, and field teams to support reliable on-road experimentation and autonomous driving data collection.