• Integrate, optimize, and operate large-scale inference systems to power AI scientific research • Build and maintain high-performance serving infrastructure that delivers low-latency, high-throughput access to large language models across thousands of GPUs • Work closely with researchers and engineers to integrate cutting-edge inference into large-scale reinforcement learning workloads • Build tools and directly support frontier-scale experiments to make Periodic Labs the world’s best AI + science lab • Make contributions to open-source LLM inference software

• Design, develop, and implement production AI/ML solutions using Vertex AI and GCP. • Build data pipelines and machine learning models integrated with BigQuery. • Implement solutions using LLMs (Large Language Models), including Claude and other generative AI platforms. • Use AI-assisted coding platforms to increase productivity and code quality. • Establish and disseminate MLOps best practices, model governance, and responsible AI principles. • Collaborate with medical and business teams to translate clinical needs into technical solutions. • Ensure compliance with LGPD (Brazilian data protection law), healthcare regulations, and information security standards. • Perform code reviews and provide technical mentorship to junior and mid-level engineers. • Document architectures, processes, and technical decisions.

• Facilitate an interview through Karat's platform • Provide input on the candidate's performance, coding style, communication skills, knowledge question answers, and coding approach • Collaborate with Karat to test content, processes, and products

• Develop and maintain high-reliability, high-performance data pipelines; • Design cloud data architectures (data lake, streaming, batch processing); • Ensure data quality, consistency, reliability, and governance; • Automate data ingestion, transformation, and loading workflows (ETL/ELT); • Work with large volumes of structured and semi-structured data; • Implement monitoring, alerting, and observability for pipelines; • Collaborate with backend and product teams to make data efficiently available; • Apply best practices for data versioning and data testing; • Optimize cost and performance of data systems in cloud environments.