Senior Machine Learning Engineer — Data Infrastructure

About Us Veeda AI is building the next generation of multimodal foundation world models for Physical AI. We're a small, fast-moving team of engineers and researchers from leading AI labs, tackling some of the most challenging problems at the intersection of AI, robotics, and embodied intelligence. If you're excited about pushing the boundaries of what's possible with Physical AI, you'll have the opportunity to make an outsized impact from day one. Responsibilities Crawling & Ingestion at Scale: Architect and own the scraping and crawling infrastructure that acquires petabytes of image and video data — designing systems that are resilient, compliant, and able to keep pace with a fast-moving content landscape. Image-Data Pipeline Design: Build and maintain production data pipelines that clean, transform, and process raw visual assets—handling format diversity, resolution variance, metadata extraction, and noise at scale. ETL/ELT for Training & Evaluation: Design ETL/ELT workflows that prepare image and video datasets for ML model training and evaluation, including annotation ingestion, quality scoring, and reproducible dataset versioning. Performance & Cost Optimization: Continuously profile and optimize pipelines for throughput, latency, and cloud storage/compute costs—ensuring research velocity without unnecessary overhead. Distributed Processing Ownership: Select, deploy, and operate distributed data-processing frameworks (e.g., Ray, Spark, Dask) to handle compute-intensive tasks like large-batch image transformation, embedding generation, and feature extraction. Cross-Functional Partnership: Work directly with Computer Vision Engineers, ML Scientists, and product teams to translate data requirements into reliable, well-documented pipelines and close the feedback loop between model performance and data quality. Technical Leadership: Lead data-pipeline design reviews, mentor junior and mid-level data engineers, establish best practices for the data team, and raise the overall engineering bar. Requirements You have 5+ years of professional experience in data engineering or a closely related discipline with a strong foundation in distributed systems, database design, and handling large binary assets (images, video) at scale. You possess strong programming skills in Python and SQL, with proven experience designing end-to-end production systems rather than basic scripts. You have designed and operated web scraping or data ingestion systems running reliably in production at significant global scale. You are hands-on with data-processing frameworks (e.g., PySpark, Pandas, Ray) and comfortable with image-processing libraries (OpenCV, Pillow, scikit-image). You have built ETL/ELT pipelines for ML/CV use cases with a strong emphasis on dataset provenance, lineage, and reproducibility. You have extensive experience with cloud object storage (AWS S3, GCP GCS) and understand the cost, latency, and durability trade-offs when managing petabyte-scale data assets. Production Quality, Agent Velocity: Your daily workflow runs through AI coding harnesses (e.g., AI agents/assistants) without sacrificing software engineering rigor. You review agent code diffs with the same scrutiny as a team member's PR, recognize AI code generation failure modes, and ship rapidly without introducing technical debt or "slop." Nice to Have Experience with Ray for distributed data processing or ML-adjacent workflows. Hands-on experience with data orchestration platforms (e.g., Airflow, Dagster, Prefect) to manage complex processing DAGs in production. Familiarity with Computer Vision concepts and image annotation formats (COCO, PASCAL VOC, YOLO). Practical knowledge of MLOps concepts, including data versioning (e.g., DVC, LakeFS) and experiment tracking for CV pipelines. Experience with containerization (Docker) and orchestration (Kubernetes) for scaling data processing workloads. Experience navigating multi-region data compliance challenges (GDPR, content licensing, and image rights across jurisdictions).

Architect and manage large-scale scraping and ingestion infrastructure for petabytes of image and video data. Design and optimize production ETL/ELT pipelines to prepare visual datasets for ML model training and evaluation.

Requires 5+ years of experience in data engineering with expertise in distributed systems and handling large binary assets. Proficiency in Python, SQL, and data-processing frameworks like Ray or Spark is essential.

Identified from the job description. Confirm important requirements above.

• Python
• SQL
• Distributed Systems
• PySpark
• Ray
• OpenCV
• ETL/ELT
• Cloud Object Storage
• Web Scraping
• Data Pipeline Design
• Image Processing
• Pandas
• Docker
• Kubernetes
• MLOps
• Data Versioning
• Dask (Software)
• Pipelines
• MLOps (Machine Learning Operations)
• General Data Protection Regulation (GDPR)
• Workflow Management
• Apache Airflow
• Resilience
• Technical Leadership
• Machine Learning Model Training
• Machine Learning Model Monitoring And Evaluation
• Data Version Control (DVC)
• AI Agents
• Research
• Artificial Intelligence
• Amazon S3
• Computer Vision
• Containerization
• Code Generation
• Data Processing
• Directed Acyclic Graph (Directed Graphs)
• Data Engineering
• Extract Transform Load (ETL)
• Data Quality
• Database Design
• Distributed Data Store
• Failure Causes
• Python (Programming Language)
• Machine Learning
• Metadata
• Operational Databases
• Pascal (Programming Language)
• Quality Scoring
• Robotics
• Software Engineering

• Data & Analytics
• Technology
• Software
• Engineering
• Science & Research
• Machine Learning Data Engineer
• Machine Learning Engineer
• Software Developers
• Computer and Information Research Scientists

Minimum experience

5+ years

Posting language

English

Working hours

40 hours per week

Location requirements

Country, Switzerland, Canada