Join an early-stage AI video team as a founding backend engineer, helping build the systems behind generative video products. You will provide technical leadership on a small team and establish strong engineering practices as the product and infrastructure grow.
Compensation
Base salary: $225,000 to $275,000 annually. Visa sponsorship is not available.
Responsibilities
Design and develop scalable backend systems for AI-powered video creation.
Build infrastructure for agentic workflows, video generation pipelines, reinforcement learning loops, and real-time data processing.
Own distributed systems architecture and improve reliability through automated testing, performance benchmarks, and end-to-end observability.
Partner with research, engineering, and design teammates to develop product features and APIs.
Write production code, set technical direction and engineering guidelines, and raise the team's engineering standards.
Lead technical QA/QC and characterization of airborne lidar datasets and derived elevation products against 3DEP, ASPRS, contractual, and project-specific requirements.
Responsibilities
Lead technical QA/QC and characterization of airborne lidar datasets and derived elevation products
Develop and implement advanced methods for evaluating lidar point density, spatial distribution, completeness, and localized deficiencies
Perform vertical accuracy assessments using GNSS, total-station, terrestrial laser scanning, UAS, and other independent reference datasets
Investigate lidar anomalies and determine whether observed deficiencies are associated with acquisition, calibration, classification, processing, or product-generation workflows
Develop scalable Python-based tools for processing tens of millions of points per tile and thousands of production datasets
Build and maintain data pipelines that generate training datasets for machine learning models and experimentation, contribute to infrastructure that supports distributed training workflows, and work with workflow orchestration tools to support multi-stage ML pipelines.
Looking for a Senior Data Engineer to build reliable data systems, shape how data is collected/used for ML systems, real-time product experiences, and analytics.
Responsibilities
Design and build scalable batch and streaming data pipelines
Build real-time data infrastructure used by production ML inference
Develop feature pipelines and feature serving for machine learning models
Create reliable datasets for analytics, experimentation, and business intelligence
Develop data models and infrastructure that support product and engineering teams
Lead the development of physics-based machine learning models and infrastructure, collaborating with simulation engineers and research scientists to deliver production-grade solutions.
Responsibilities
Work closely with simulation engineers and research scientists
Transform prototype implementations to robust production-grade model implementations, libraries and tooling
Work with multi-GPU/multi-node distributed systems for training and inference
Optimize models and pipelines at all levels
Design and build scalable infrastructure for end-to-end ML applications on cloud
The AI / ML DevOps Engineer designs, develops, deploys, and supports scalable machine learning solutions that enable advanced analytics and AI capabilities across HonorHealth.
Responsibilities
Design and implementation of the environment that deploys scalable AI/ML solutions, including predictive models, large language model use cases, and agentic workflows that support clinical, operational, and business objectives.
Develops and maintains end-to-end machine learning and RAG pipelines supporting data ingestion, application feature engineering, training, evaluation, deployment, and lifecycle management.
Architects and supports production-grade DevOps practices, including CI/CD, model versioning, automated testing, monitoring, alerting, retraining, and rollback strategies.
Deploys and manages AI/ML applications in cloud environments, ensuring solutions are secure, reliable, performant, and operationally supportable.
Implements observability and performance evaluation practices to manage compute resource utilization, performance, and cost efficiency.
Senior Staff ScientistNewUniversity of Toronto · Toronto, Canada
Senior Software Engineer, AI/ML NLP, Deep Learning, Search InfrastructureGoogle · Mountain View, US_(1)_(1).png?1723110055)
Python Software Engineer - Data InfrastructureMan Group · London, United Kingdom
Senior Data EngineereComID · Stockholm, Sweden
Senior Research Machine Learning EngineerPhysicsX · London, United Kingdom