Senior Software Engineer, Cloud, Simulation

Wayve · Robotics · London, United Kingdom · Simulation, Evaluation, Validation

Senior Software Engineer role focused on Wayve's Simulation Technology team, which develops and evaluates the company's driving intelligence. The role involves evolving the simulation platform, integrating classical simulation techniques with machine learning, and working on robot emulator fidelity, visual fidelity, or efficient scaling. Responsibilities include owning KPIs for simulator cost and throughput, leading technical discussions, and implementing production-quality software. Requires experience with workflow orchestration, Python, and Kubernetes, with desirable experience in autonomous vehicles, ML inference systems, and modern machine learned graphics.

What you'd actually do

  1. Own key performance indicators (KPIs) for simulator cost, SLOs, throughput, latency, etc.
  2. Work cross-company on aligning technical dependencies for simulator implementation
  3. Lead technical discussions and guide technical direction
  4. Effectively integrate the components of the simulated robot into the simulation platform
  5. Effectively integrate machine-learned graphics subsystems into the simulation platform
  6. Implement production quality software in Python

Skills

Required

  • Experience with workflow orchestration systems (e.g. Airflow, Dagster, Flyte, etc.) and/or developing data intensive applications
  • Excellent development skills in Python
  • Deep knowledge of Kubernetes at the user level
  • Good sense of systems and data oriented software engineering design - what makes code reusable and extensible
  • Understanding of common software performance issues and design tradeoffs
  • 5+ years of industry experience designing and programming software
  • Excellent communication and people engagement skills

Nice to have

  • Experience in the field of autonomous vehicles or robotics
  • Proficiency with other programming languages like go or C++
  • Experience scaling simulations or data intensive workloads
  • Experience with design, implementation, and optimization of large-scale machine learning inference systems running in cloud GPU environments
  • Experience operating and scaling modern machine learned graphics techniques (NeRF, Gaussian Splatting, or GenAI)

What the JD emphasized

  • production quality software
  • machine learning inference systems
  • modern machine learned graphics techniques

Other signals

  • simulator
  • driving intelligence
  • machine learning
  • robot emulator fidelity
  • visual fidelity
  • efficient scaling
  • robotics
  • data teams
  • simulation platform
  • machine-learned graphics subsystems
  • production quality software
  • workflow orchestration systems
  • data intensive applications
  • Kubernetes
  • software performance issues
  • autonomous vehicles
  • machine learning inference systems
  • cloud GPU environments
  • modern machine learned graphics techniques