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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates strong production software engineering capabilities in Python, focusing on building maintainable backend services and APIs for data-intensive workloads. Proficient in designing distributed systems and ensuring reliability and performance across large-scale data processing and model monitoring.
Highest-signal resume keywords
Python Software EngineeringDistributed Systems DesignModel Monitoring and EvaluationCross-Team CollaborationOperational Tooling and Reliability
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Backend Services DevelopmentData Processing SystemsModel Execution WorkflowsScalability ImprovementReliability Investigation3D Geometry UnderstandingSensor Data Validation3D Object DetectionSegmentationTracking
Soft Skills
Clear CommunicationEffective CollaborationMeaningful Ownership
Tools & Technologies
FlyteAirflowDagsterArgoSparkDatabricksRayKubernetesGPU InfrastructureML-Platform Systems
Industry Keywords
Data Enrichment PlatformRoboticsComputer VisionAutonomous VehiclesPetabyte-Scale Data
Tech Stack
Tools & technologiesAirflowDistributed SystemsKubernetesPythonRaySpark
About the role
Key responsibilities & impact- Design, build and operate backend services, APIs and data-processing workflows for the Data Enrichment Platform
- Develop model-execution workflows, versioned outputs, annotation capabilities and dataset-catalogue features
- Improve scalability and reliability for large-scale inference, automated evaluation and model monitoring
- Investigate and resolve reliability, performance and cost issues across petabyte-scale data and GPU-backed workloads
- Contribute to designs covering data locality, scheduling, capacity, backpressure, failure recovery and observability
- Collaborate with model engineering, AI Platform, infrastructure, compute, storage and data teams
- Write maintainable, tested and observable software while improving documentation, operational tooling and engineering standards
- Build scalable infrastructure for inference, automated evaluation, model monitoring, active learning and retraining
- Enable repeatable workflows, reproducible dataset selection, versioned outputs, annotation workflows and dataset catalogues
- Provide monitoring and operational tooling for platform reliability, throughput, cost and workflow status
- Support continuous evaluation, drift detection and automated model-improvement loops across robotics and computer-vision workloads
Requirements
What you’ll need- Strong production software-engineering experience in Python, including building maintainable, tested and observable backend services, APIs or data-processing systems
- Experience designing, building or operating distributed systems for data-intensive or compute-intensive workloads
- Experience delivering reliable software from design through deployment and production operation
- Good system-design fundamentals and ability to make sound technical trade-offs within a complex platform
- Comfortable investigating unfamiliar systems and diagnosing reliability, performance or operational issues
- Clear communication, effective cross-team collaboration and meaningful ownership of production components
- Experience with Flyte, Airflow, Dagster, Argo, Spark, Databricks, Ray, Kubernetes, GPU infrastructure or ML-platform systems would be an advantage
- Hands-on experience with robotics, autonomous-vehicle or computer-vision datasets, including sensor data, annotations or model outputs
- Practical understanding of 3D geometry for robotics or computer vision, including coordinate frames, rigid transformations, camera calibration and projection of 3D points into images
- Experience validating sensor data or model outputs, including investigating spatial or temporal misalignment and implementing checks for correctness
- Specific experience with 3D object detection, segmentation, tracking or ego motion would be an advantage
- Experience developing or training perception models is not required
- Eligibility to work in the country where applying is required by the application form
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