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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building and optimizing ingestion-time models for autonomous vehicles, leveraging strong Python and Spark skills to manage large-scale data efficiently. Proven ability to collaborate across teams to define data strategies and deliver measurable storage-cost savings through innovative ML-driven solutions.
Highest-signal resume keywords
Autonomous-Vehicle ExperienceML Engineering ExperiencePython ProgrammingSpark SkillsData Selection and Filtering
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
ML Model DevelopmentData ScoringData FilteringQuantitative AnalysisData ProvenanceData RetentionComputer VisionIoT Data ManagementHeuristic DevelopmentCloud Data Engineering
Soft Skills
High-Ownership ApproachPragmatic Decision-MakingCross-Functional CollaborationLeadership Without Authority
Industry Keywords
Large-Scale DataData PolicyData DistributionStorage-Cost SavingsEdge FilteringVehicle-Side FilteringData AnnotationData QualityAV RoboticsModel Training
Tech Stack
Tools & technologiesCloudIoTPythonSpark
About the role
Key responsibilities & impact- Build and own the ingestion-time model that scores incoming driving data and routes it to hot, cold, archive or delete
- Work with training teams to define the right data distributions for every model being built
- Deliver early storage-cost savings through practical heuristics, then develop ML-driven filtering
- Partner with ingestion, annotation, retrieval, data quality, model training, AV/robotics, evaluation and storage teams to set company-wide data policy
- Lead and work hands-on with a small group of software and ML engineers to build data provisioning and data provenance features into Corpus
- Define dataset content strategy, including required data and metadata
- Steer data selection based on model-engineering needs and prioritize valuable scenarios for hot storage
- Deliver measurable storage-cost savings as ingestion scales across internal fleet, partner and OEM data
- Evolve from practical heuristics to ML-driven filtering and influence filtering at the vehicle and edge
- Work with the Corpus Implementation Engineer to turn strategy into production systems
Requirements
What you’ll need- Strong autonomous-vehicle or robotics experience, working with large-scale driving, sensor or comparable data
- Principal, hands-on ML engineering experience building models that score, select or filter data
- Experience operating at petabyte scale and comfort with the associated engineering
- Strong Python and Spark skills, with large-scale cloud data experience
- Strong quantitative intuition and ability to make practical, measurable decisions
- Pragmatic, high-ownership approach and ability to set direction across several teams without direct authority
- Experience in data selection, retention or filtering for model training, edge or vehicle-side filtering, computer vision or high-volume IoT data, or leading small cross-functional groups is a bonus
- Passion for autonomy and willingness to learn encouraged even if not every requirement is met
Benefits
Comp & perks- Salaries benchmarked against the market annually
- Meaningful equity, sharing in the ownership and long term success of Wayve
- Relocation support and visa sponsorship where applicable
- Hybrid working, core hours and the chance to work hands on in vehicle workshops and labs
- Learning and development budgets with support for training, conferences and growth
- Comprehensive benefits including health insurance, dental, enhanced maternity and paternity leave, retirement or pension where applicable, access to therapists, wellbeing partnerships, team socials and more