Remote Software engineering Jobs · ML

• Design, build, evaluate, and ship LLM based solutions that tell stories about content and users.
• Collaborate with cross-functional teams to build new product features.
• Promote best practices of ML systems development and evaluation throughout the organization.

• Design, implement, and deploy AI-powered features, including model training, fine-tuning, and prompt engineering workflows.
• Translate product requirements into robust, production-ready AI solutions, working with Product Managers, Software Engineers, and Data Scientists.
• Optimize models and infrastructure for scalability, latency, and cost efficiency, partnering with DevOps and MLOps to ensure reliable and maintainable AI pipelines.

Design scalable software solutions with a focus on quality, performance, and maintainability. Lead software architecture decisions, ensuring the design aligns with business requirements and technical standards. Collaborate with Research Leads and Product Owners to gather requirements, design MLOps pipelines and coordinate the software delivery that adds value to the business.

Design and prototype machine learning solutions to improve the software development workflow. Apply existing open-source models or train custom ones as needed. Build training and evaluation pipelines that support fast iteration and ensure reproducibility.

• Train and fine-tune text models (LLMs), deploying them on Google Cloud, and building automation around these models.
• Analyse business requirements for the desired output format and the logic the model must implement.
• Develop services and APIs (REST/gRPC) that expose the model to other systems.

• Lead the design, development, and deployment of AI/ML models.
• Build and maintain production-grade ML pipelines and model monitoring.
• Mentor and develop junior AI engineers, laying the foundation for AI best practices.

• Develop Machine Learning models and pipelines.
• Interact with databases and data infrastructure.
• Conduct in-depth data analytics and build statistical data inference models of biological processes.

• Build infrastructure that enables quality monitoring across Design Generation.
• Guide Design Generation teams on how to evaluate their systems effectively.
• Build platforms that make evaluation accessible and automated.

• Lead AI engineering execution across the build organization.
• Partner with the Director of Product - AI.
• Define how millions of people experience automation in the AI era.

• Lead development of models for autonomous vehicles to perceive and interpret road geometry.
• Define next-generation deep learning architectures for extracting road and lane semantics from sensor data.
• Develop evaluation frameworks for lane and road geometry accuracy and cross-domain generalization.