Technical Lead, Search & Index

At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition  (including breastfeeding) or any other basis as protected by applicable law.  

About us   

Founded in 2017, Wayve is the leading developer of Embodied AI technology.  Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems.

Our vision is to create autonomy that propels the world forward.  Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving. 

In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future.

At Wayve, your contributions matter.  We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact.  

Make Wayve the experience that defines your career!  

The role 

We are hiring a Technical Lead for our Search & Indexing team, responsible for making Wayve’s massive corpus of multimodal video data discoverable and searchable. This team builds the indexing infrastructure that powers vector-based and metadata-based retrieval - enabling high-leverage workflows across data curation, training set construction and ML research.

As a Tech Lead, you’ll design, scale, and evolve the system that supports retrieval across hundreds of thousands of hours of driving data. You’ll contribute to architectural decisions, own complex components, and help define best practices as the team grows.

You’ll play a key role in launching the next version of our indexing stack, supporting basic similarity search and metadata filtering across hundreds of thousands of hours of video and sensor data. The immediate focus is to move quickly: leverage off-the-shelf tools, help inform build vs. buy decisions, and deliver an MVP system in months — not quarters.

This role requires someone who is comfortable in ambiguity, technically pragmatic, and able to make strong architectural decisions in imperfect conditions. You’ll collaborate with ML teams, platform engineers, and downstream users to turn indexing from a bottleneck into a core capability.

 

Key Responsibilities

• Design and implement an MVP indexing system to support vector similarity search and metadata filtering
• Evaluate and integrate off-the-shelf solutions (e.g., Faiss, LanceDB); prototype quickly
• Lead technical exploration of buy vs. build tradeoffs for long-term retrieval infra
• Own components of the indexing pipeline from prototype to production
• Collaborate with ML engineers and data curation teams to understand embedding formats and retrieval needs
• Work closely with infra/platform teams to ensure the MVP is observable, scalable, and extensible
• Contribute to team engineering culture and mentor others as the team scales

 

About you  

In order to set you up for success at Wayve, we’re looking for the following skills and experience:

Essential

• Over 10 years of experience in backend, infra, or ML systems roles, with a track record of launching working systems quickly
• Deep familiarity with vector similarity search frameworks and metadata indexing
• Strong coding skills in Python or other systems languages
• Experience shipping MVPs in ambiguous problem spaces, ideally in 0 to 1 or exploratory domains
• Ability to make architectural tradeoffs and move fast without sacrificing future extensibility
• Comfortable working across system boundaries (infra, ML, data curation)

Desirable

• Experience with one or more: Faiss, Pinecone, LanceDB, Databricks Vector
• Familiarity with multimodal data (video, sensor, metadata) or large-scale ML pipelines
• Exposure to embedding generation, active learning, or retrieval-augmented training
• Prior experience in autonomy, robotics, or large-scale data infrastructure
• Ability to inform technical purchasing decisions and interface with external vendors

This is a full-time role based in our office in