Software Engineer (Data Flywheel Platform)

Wayve; Sunnyvale (CA); On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

Private healthcare

Paid time off

Mental health resources

Learning & development

Job summary

Wayve is hiring a senior Software Engineer to build the data platform and infrastructure powering its AI flywheel and foundation-model stack. You will own the systems enabling world-scale data curation, training, and evaluation across multiple teams.

The role partners with scientists and ML engineers to push production-ready scale and reliability. You’ll design and scale distributed pipelines, leverage Spark, Databricks, and vector tooling, and drive observability, testing, and deployment.

Qualifications

• Proven production software engineering in Python services and APIs.
• Strong CS fundamentals and experience shipping scalable systems.
• Experience with large-scale data processing and ML workflows.

Responsibilities

• Build and scale data platforms powering model training and evaluation.
• Own distributed training, inference, and serving infrastructure.
• Develop self-serve, observable products used by multiple teams.

Skills

CS fundamentals

Production Python

Distributed systems

SQL optimization

Kubernetes

Education

CS degree

Tools

Flyte

Airflow

Dagster

Spark

Databricks

Ray

Lance

Iceberg

Job description

• This role sits in the AI Platform organization, on the data flywheel that powers every model we ship
• Applied Scientists and ML Engineers on the team push the frontier on data curation, enrichment, foundation-model evaluation, and the models themselves
• This role builds the platform underneath all of it: the pipelines, infrastructure, and systems that turn world-scale fleet data into high-signal training data, evaluate and train foundation models, and enable every team to run these workflows themselves
• As deployment scales, the leverage is enormous: the better the platform, the faster the whole flywheel turns
• We are hiring a senior Software Engineer to build the platform that powers Wayve’s data flywheel and foundation-model stack
• This is the engineering counterpart to our Applied Scientist and ML Engineer roles: you build the systems they, and the wider company, depend on
• It is high-leverage, high-visibility work with a clear path to deep system ownership
• Build the systems that allow teams to turn world-scale driving data into high-signal training data, and evaluate and train foundation models on it
• Replace ad-hoc scripts and manual handoffs with self-serve, observable products used across Science, Autonomy, and Evaluation
• Every model Wayve ships runs on this platform: your work compounds across the entire fleet and roadmap
• Work shoulder to shoulder with a world-class science and engineering team, with real deployment at global OEM scale (Nissan, Stellantis, Uber)
• TC3 / TC4 ownership of platform and infrastructure, with room to set technical direction as the platform matures
• Build and scale the data curation and enrichment pipelines that turn world-scale fleet data into high-signal training data: mining and active-learning loops, running model-based enrichments over billions of rows, and ensuring data quality at scale
• Build the evaluation infrastructure behind foundation-model progress: harnesses for offline and closed-loop evaluation, metric and benchmark pipelines, and world-model-based evaluation
• Build and optimize training and serving infrastructure for large pretrained models: distributed training, batched inference, and large-scale model backfills
• Build the data-platform backbone: distributed data processing (Ray Data, Daft, Spark / Databricks), embedding and vector search (turbopuffer, Milvus), lakehouse formats (Lance, Iceberg), dataset versioning, and the enrichment and annotation catalog
• Make it self-serve and reliable: turn one-off processes into products that other teams operate themselves, and own testing, observability, and on-call for what you ship
• Partner closely with Applied Scientists and ML Engineers to take research from prototype to production at scale

Benefits

• Private healthcare: Choose our optional health insurance for comprehensive coverage for you and your family.
• Paid time off: Paid vacation plus public holidays and additional leave programs, ensuring you have time to unwind.
• Mental health resources: Through Spill, you can access therapy and mental health support.
• Community and socials: Join clubs or attend team socials to connect over hobbies, sports, or just for fun.
• Competitive compensation: Our compensation package includes cash and equity, making you a true partner in our success.
• Learning and development: Budgets for books, courses, and company-wide training to support your continuous growth.

Requirements

Seniority to match the level: takes ambiguous, cross-team problems and drives them to completion, and at TC4 sets technical direction and multiplies the teamA track record of shipping and operating production systems that other teams depend on: testing, code review, observability, and on-callStrong CS fundamentals and several years of production experience (roughly 6 or more for TC3, more for TC4), or equivalent; a degree in CS or comparable practical experienceLarge-scale data and distributed-systems experience: batch and streaming pipelines, workflow orchestration (Flyte, Airflow, Dagster, or similar), and distributed processing (Spark / PySpark, Ray, Databricks, or equivalent)

• Systems design for scale: reliable, observable, high-throughput data or ML systems, with strong SQL and query and performance optimization
• Strong production software engineering, especially production Python (services, APIs, large-scale data processing), and comfort owning and extending large codebases
• ML platform / MLOps: model registration, distributed training, and inference or serving optimization
• Autonomous driving, robotics, or other large-scale sensor-data workflows
• Embedding and vector search, annotation tooling, or feature and data catalogs
• Enough exposure to foundation models, world models, or ML evaluation to partner deeply with scientists
• Kubernetes and modern data / lakehouse stacks (Databricks, Lance, Iceberg)

If you’re passionate about self-driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply