Senior Data & ML Engineer — Mexico

Empresa: Self Inspection

About SelfInspection SelfInspection is an AI-powered vehicle inspection platform transforming how rental companies, fleets, leasing companies, financial institutions, and automotive businesses inspect and assess vehicles. Our platform processes large volumes of vehicle images, videos, inspection data, AI predictions, and customer data to automatically identify vehicle condition, damage, and other important attributes. As our platform grows, we are looking for a Senior Data & ML Engineer who can build the data infrastructure behind our AI systems and help us reliably move ML models from experimentation into production. This is a hybrid role sitting between Data Engineering and Machine Learning Engineering. What You’ll Do Data Engineering - Design and build scalable data pipelines for structured and unstructured data, including - inspection records, images, videos, AI predictions, and telemetry - Build reliable ETL/ELT pipelines for analytics, reporting, model training, and ML - inference - Design and maintain data models, schemas, and storage architecture - Work with large datasets stored across relational databases, object storage, data - warehouses, and ML datasets - Build automated data validation and data-quality monitoring - Optimize pipelines for performance, reliability, and cost - Build datasets and data pipelines used by AI/Computer Vision engineers for model - training and evaluation - Implement lineage, versioning, and reproducibility for ML datasets - Help establish strong data governance and data lifecycle practices ML Engineering / MLOps - Build infrastructure for training, evaluating, versioning, and deploying ML models - Develop production pipelines connecting ML models to backend systems - Build and maintain batch and real-time inference pipelines - Implement model monitoring, including performance, latency, failures, data drift, and - model-quality metrics - Build automated model evaluation and deployment workflows - Manage model and experiment tracking using tools such as MLflow - Help automate retraining and model promotion processes - Work with AI engineers to turn experimental Python models into reliable production - services - Improve GPU/CPU inference performance and infrastructure utilization - Help define standards for ML observability, reproducibility, and production readiness What You’ll Help Us Build Our ML/data platform processes: - Vehicle inspection images and videos - Computer Vision model predictions - Vehicle damage detections - VIN and vehicle metadata - Tire and wheel information - Inspection reports - Customer and fleet data - Model training datasets - Model evaluation and validation results You will help us create the infrastructure that connects this data across the full lifecycle: Data Collection → Validation → Storage → Dataset Creation → Model Training → Evaluation → Deployment → Inference → Monitoring → Retraining What We’re Looking For Strong Data Engineering Experience - 5+ years of professional software/data engineering experience - Strong Python - Strong SQL and relational database experience, preferably PostgreSQL - Experience designing production ETL/ELT and distributed data pipelines - Experience processing high-volume datasets - Experience with object storage such as S3 or Google Cloud Storage - Experience with data warehouses such as BigQuery, Snowflake, Redshift, or similar - Strong understanding of data modeling, partitioning, indexing, and query optimization - Experience with orchestration tools such as Airflow, Dagster, Prefect, or similar - Experience with event-driven systems such as Kafka, Pub/Sub, RabbitMQ, or similar ML Engineering Experience You don’t need to be a research scientist, but you should understand how ML systems work in production. We expect experience with several of the following: - PyTorch or TensorFlow - MLflow, Weights & Biases, or similar experiment/model tracking systems - Model serving and inference pipelines - Dataset and model versioning - ML training pipelines - Model evaluation - Model monitoring and data drift - Docker and containerized ML workloads - GPU-based workloads - CI/CD for ML systems Cloud & Infrastructure Experience with: - AWS and/or Google Cloud Platform - Docker - Kubernetes or managed container platforms - Cloud storage and distributed compute - Infrastructure monitoring and observability - CI/CD pipelines Experience with technologies such as Vertex AI, SageMaker, Databricks, Spark, Ray, or Kubernetes-based ML workloads is a plus. Nice to Have - Experience working with Computer Vision systems - Experience processing large image/video datasets - Experience with YOLO or other object-detection models - Experience optimizing GPU inference - Experience building feature or dataset stores - Experience with annotation pipelines and dataset management - Experience with model drift and production ML monitoring - Experience building ML platforms used by multiple AI engineers - Experience working in automotive, fleet, inspection, insurance, or mobility technology What Success Looks Like Within this role, you will help us create a data and ML infrastructure where: - AI engineers can easily create and version training datasets - Experiments are reproducible - Models can move from development to production reliably - Training and inference pipelines are automated - Data quality issues are detected before they impact models - Production model performance is measurable - Model versions and datasets can be traced back to individual predictions - Our ML infrastructure can scale as inspection volume grows The Person We’re Looking For We are looking for someone who enjoys working at the intersection of software engineering, data engineering, and machine learning. You should be comfortable discussing data architecture with backend engineers in one meeting and debugging an ML inference pipeline with AI engineers in the next. You don’t need to invent new neural network architectures. You do need to know how to build the infrastructure that allows ML teams to train, deploy, monitor, and continuously improve models reliably at scale. Why Join SelfInspection? You’ll have an opportunity to influence the architecture of a real-world AI platform where ML is a core part of the product—not an experimental side project. You’ll work closely with our AI, backend, product, and engineering teams and have significant ownership over how our data and ML platform evolves as the company scales. Due to the high volume of applications we receive, we're unfortunately unable to respond to every applicant individually. If you have not been contacted within two weeks of submitting your application, please assume that your application has not been selected to move forward at this time.

5+ años de experiencia profesional en ingeniería de software/datos

Python, SQL/PostgreSQL, ETL/ELT, procesamiento de datos a gran escala, almacenamiento en la nube (S3, GCS), warehouses (BigQuery, Snowflake, Redshift), orquestación (Airflow, Dagster, Prefect), sistemas de eventos (Kafka, Pub/Sub, RabbitMQ), PyTorch/TensorFlow, MLflow, Docker, Kubernetes, AWS/GCP, CI/CD

Capacidad para colaborar entre ingeniería de software, datos y ML; buenas habilidades de comunicación; orientación a producción y escalabilidad

Diseñar y construir pipelines de datos escalables; construir infraestructura para entrenamiento, evaluación, versionado y despliegue de modelos ML; monitorear modelos en producción; automatizar flujos de retraining y promoción; colaborar con ingenieros de IA para llevar modelos a producción

Inglés - Avanzado