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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 operating scalable data pipelines and distributed data processing systems, with strong proficiency in Python, SQL, and PySpark. Capable of collaborating effectively with interdisciplinary teams to enhance machine learning workflows and data quality assurance.
Highest-signal resume keywords
Scalable Data Pipeline DevelopmentPython Software EngineeringSQL and PySpark ProficiencyWorkflow Orchestration with AirflowUnderstanding of Autonomous Driving Data
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data Pipeline DevelopmentDistributed Data ProcessingPython ProgrammingSQL ProficiencyPySpark ExpertiseData Quality AssuranceMachine Learning WorkflowsData TransformationScenario MiningModel Evaluation
Soft Skills
Excellent CommunicationCollaborative Skills
Tools & Technologies
AirflowFlyteRayOLAP ConceptsDAG-Based Systems
Industry Keywords
Autonomous Driving DataSensor CharacteristicsTimestampingCoordinate TransformationsCalibrationEgo-Motion SignalsThird-Party Dataset IngestionScenario TaxonomiesEvent TaggingSemantic Understanding
Tech Stack
Tools & technologiesAirflowPySparkPythonRaySparkSQL
About the role
Key responsibilities & impact- Design and deliver scalable data pipelines that turn diverse internal and external data into structured, reliable and model-ready datasets
- Support data ingestion, data quality assurance, transformation, curation, evaluation and machine learning workflows
- Bring up ingestion pipelines for new vehicle platforms and sensor configurations
- Build and improve pipelines supporting model development, evaluation and production ML workflows for autonomous driving
- Ingest, transform and curate large-scale real-world, synthetic and partner-provided datasets
- Develop data quality checks, validation processes and monitoring for raw vehicle data and processed datasets
- Curate and mine real-world and synthetic data to drive scenario diversity, coverage and feature-specific development
- Improve pipeline performance, reliability and usability, reducing bottlenecks and increasing ML iteration velocity
- Collaborate with ML engineers, Data Corpus, AI Platform and external partners to integrate pipelines with production-scale learning systems
Requirements
What you’ll need- Proven experience building and operating scalable data pipelines or distributed data processing systems in production environments
- Strong software engineering skills in Python, with a solid foundation in maintainable, reliable, and well-tested software development practices
- Proficient in SQL and PySpark, with experience using warehouse/OLAP concepts, window functions, and Spark for distributed data processing
- Experience with modern data pipeline architectures, including workflow orchestration and DAG-based systems such as Airflow, Flyte, Ray, or similar
- Solid understanding of robotics and automated driving data concepts, including sensor characteristics, timestamping and clock synchronisation, coordinate transformations, calibration, and ego-motion signals such as GNSS/IMU and vehicle odometry
- Understanding of machine learning development workflows, including training data generation, evaluation datasets, scenario mining, and model iteration
- Excellent communication and collaborative skills, capable of working effectively with interdisciplinary teams
- Prior work in calibration, perception, imitation learning, or trajectory prediction (desirable)
- Experience with third-party dataset ingestion and transformation (desirable)
- Familiarity with automated driving data and scenario taxonomies, including ODD definitions, manoeuvre and behavior labels, scene classification, event tagging, semantic understanding (desirable)
Benefits
Comp & perks- 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
- Salaries benchmarked against the market annually