Principal Engineer, Model Development Platform

Wayve; Sunnyvale (CA); Hybrid; USD 296,000 - 335,000; Full time

Benefits offered by this job

Equity package

Job summary

Wayve is seeking a Principal Engineer for the Model Development Platform in Sunnyvale, CA. You’ll own the end-to-end architecture behind the AI model lifecycle—from data ingestion and training to experiment scheduling and on-road testing—ensuring reliability, scalability, and safety for rapid research and deployment.

You’ll lead cross-domain technical direction with ML Ops, data pipelines, and optimization algorithms, mentor engineers, and partner with Product, Research, and Operations to align

Qualifications

• 10+ years designing and building large-scale distributed systems, ML/AI infrastructure, or development platforms.
• Ability to design systems spanning web platforms, ML pipelines, and large-scale compute orchestration (Spark, Ray, Kubernetes, Airflow, MLflow).
• Experience driving reliability, SLAs/SLOs, and observable systems at scale (four nines or better).
• Hands-on systems design with production-quality code and API design.

Responsibilities

• Own system architecture and reliability for the model development platform.
• Lead cross-domain technical leadership across front-end UIs, distributed training, and data pipelines.
• Hands-on problem solving; lead architectural reviews and pragmatic solutions.
• Build scheduling and experiment systems balancing hardware, safety, and research priorities.

Skills

Technical Leadership at Scale

Architectural Depth & Breadth

Reliability & Performance

Hands-On Systems Design

Collaborative Influence

Mentorship & Culture

Tools

Kubernetes

Spark

Airflow

Ray

MLflow

Job description

As Principal Engineer for the Model Development Platform, you’ll own the end-to-end architecture behind Wayve’s AI model lifecycle, from data ingestion and training to experiment scheduling and on-road testing. Working at the intersection of AI research, large-scale distributed systems, and robotic operations, you’ll keep the platform reliable, scalable, and coherent so our researchers and engineers can iterate fast and deploy autonomous driving models safely.

Partnering with the Head of Model Dev Platform, you’ll set and execute the technical vision, aligning infrastructure and tooling with company goals. You’ll lead by example, going deep across web applications, distributed compute, ML Ops, data pipelines, and optimization algorithms, and through architecture and mentorship you’ll enable teams to build platform capabilities that measurably accelerate model development and fleet learning.

What you’ll own

• System architecture & reliability - Design and evolve the platform’s overall architecture for reliability, observability, and scalability. Set performance, latency, and availability targets, and drive the engineering standards to meet them.

• Cross-domain technical leadership - Unify the platform across disciplines, from front-end UIs and distributed training to Spark data pipelines and optimization-based experiment scheduling, ensuring systems interoperate cleanly.

• Hands-on problem solving - Dive into the hardest challenges across subteams, lead architectural reviews, and propose pragmatic solutions that balance innovation with operational simplicity.

• Experimentation & scheduling systems - Build systems that optimize how models are tested in simulation and on-road, using techniques like linear programming and heuristic optimization to balance hardware, safety, and research priorities while improving throughput and turnaround.

• Data & compute infrastructure - Architect pipelines that ingest, transform, and enrich petabytes of fleet sensor data, and drive efficient compute use across GPU, CPU, cloud, and edge for both prototyping and large-scale training.

• Strategic collaboration - Partner with Product, Research, and Operations to align architecture with user needs and co-own the platform’s long-term roadmap.

About You

Essential

• Technical Leadership at Scale – 10+ years of experience designing and building large-scale distributed systems, ML/AI infrastructure, full stack web application, or developer platforms, including at least 3 years as a staff or principal-level engineer.

• Architectural Depth & Breadth – Proven ability to design systems spanning web platforms, ML pipelines, and large-scale compute orchestration (e.g., Spark, Ray, Kubernetes , Airflow, MLflow).

• Reliability and performance – Experience driving platform reliability improvements, defining SLAs/SLOs, and building self-healing and observable systems that operate at “four nines” availability or better.

• Hands-On Systems Design – Deep understanding of distributed computing, workflow orchestration, data modeling, and API design, with the ability to write and review production-quality code.

• Collaborative Influence – Excellent communication and cross-functional collaboration skills; ability to guide engineers, managers, and researchers toward unified technical direction.

• Mentorship & Culture – Demonstrated success in mentoring engineers across levels and cultivating a culture of engineering excellence.

Desirable

• Optimization & Scheduling Expertise – Experience applying algorithmic or mathematical optimization (e.g., linear programming, graph algorithms) to operational or scheduling problems.

• ML Ops & Experimentation Systems – Familiarity with end-to-end model lifecycle tooling, from data ingestion and training CI to model artifact tracking and evaluation workflows.

• Domain Experience – Prior exposure to autonomous systems, robotics, or other safety-critical domains.

• Full-Stack Fluency – Experience with modern web frameworks (e.g., React, Flask, FastAPI) and how they integrate into backend systems.

• Data Governance – Understanding of data privacy, compliance, and secure handling practices for large-scale sensor data.

This role is a full-time role based in Sunnyvale, CA (hybrid) and the reasonably estimated salary for this role ranges from $295,500 to $335,300, plus a competitive equity package. Actual compensation is based on the candidate’s skills, qualifications, and experience.