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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in leading the technical vision and architecture for large-scale data platforms, with a focus on backend services, APIs, and model-execution workflows. Proven ability to mentor engineers and drive cross-team collaboration while ensuring high standards for software quality and operational efficiency.
Highest-signal resume keywords
Python ProgrammingLarge-Scale Distributed SystemsBackend Services DevelopmentWorkflow OrchestrationPeople Leadership
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Software EngineeringData Processing SystemsModel DeploymentObservabilityVersion ControlData Quality ControlArchitectural DesignScalable Platform DevelopmentAutomated EvaluationInference Cost Reduction
Soft Skills
MentoringTechnical DirectionCross-Team CollaborationFeedback DeliveryPriority Setting
Tools & Technologies
FlyteAirflowDagsterArgoSparkDatabricksRayKubernetesGPU InfrastructureAnnotation Platforms
Industry Keywords
Data Enrichment PlatformModel EngineeringAI PlatformCompute InfrastructureData LocalityCapacity ManagementFailure RecoveryObservabilityPetabyte-Scale DataSelf-Service Products
Tech Stack
Tools & technologiesAirflowDistributed SystemsKubernetesPythonRaySpark
About the role
Key responsibilities & impact- Own the technical vision and architecture for the end-to-end Data Enrichment Platform
- Lead the design, development and operation of backend services, APIs, model-execution workflows, versioned outputs, annotation capabilities and dataset catalogues
- Make architectural decisions for petabyte-scale data and large GPU fleets, addressing data locality, scheduling, capacity, backpressure, failure recovery, observability and cost
- Evaluate whether to build, buy, reuse, integrate, consolidate or replace technologies
- Establish technical boundaries and ownership across model engineering, AI Platform, infrastructure, compute, storage and data teams
- Lead cross-team delivery while remaining hands-on
- Mentor senior engineers and raise standards for maintainable, tested and observable software
- Lead and develop a small team of engineers through technical direction, coaching, feedback and priority setting
- Drive adoption of reusable, self-service capabilities
- Define measurable service levels and success metrics for reliability, throughput, cost, automation and adoption
- Build scalable platforms for large-scale inference, automated evaluation, model monitoring, active learning and retraining
- Develop reproducible dataset selection, versioned outputs, annotation workflows and dataset catalogues with metadata, provenance and lineage
- Operate model-execution workflows across petabytes of driving data and large GPU fleets
- Build monitoring and operational tooling for model quality, platform reliability, throughput, cost, runtime and workflow status
- Establish foundations for continuous evaluation, drift detection and automated model-improvement loops
- Deliver architectural improvements that reduce inference costs, duplicate compute and manual operational effort
Requirements
What you’ll need- Owned the long-term technical direction of a complex, multi-system platform and demonstrated measurable outcomes
- Strong production software-engineering experience in Python
- Experience building maintainable, tested and observable backend services, APIs and data-processing systems
- Deep experience designing and operating large-scale distributed systems for data-intensive or compute-intensive workloads
- Track record of turning internal platforms into widely adopted, reliable and usable self-service products
- Excellent architecture and technology judgement
- Experience leading delivery across organisational boundaries and aligning teams around ownership and shared outcomes
- Experience mentoring senior engineers
- People leadership experience, such as managing or technically leading a small team of 2-3 engineers
- Experience with workflow orchestration, batch inference, ML platforms, model deployment or registries is advantageous
- Relevant technologies may include Flyte, Airflow, Dagster, Argo, Spark, Databricks, Ray, Kubernetes and GPU infrastructure
- Experience with annotation or dataset-catalogue platforms, including provenance, versioning, lineage and quality control, is valuable
- Experience with production robotics or computer-vision platforms is particularly desirable
- Computer-vision research expertise is not required
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