• Development of Pruna’s SaaS Platform • Contribute to the development and maintenance of Pruna’s SaaS cloud platform, designed for model compression and benchmarking • Design and develop responsive, user-friendly interfaces using basic frontend technologies • Implement and optimize backend services to support the platform’s functionalities, including model compression, performance evaluation, and user interaction through the UI • Collaborate with software engineers, ML engineers, and product teams to ensure seamless integration and high-quality user experience • Oversee the R&D cluster of the company, including individual machines on AWS that users can start and work on daily • Manage and maintain cluster for ML workload, facilitating the execution of batched experiments and jobs for the team • Ensure optimal performance, scalability, and reliability of the compute resources used for research and development • Scale ML Research Engineers' work, manage experimentation setups, pipelines, and testing environments • Collaborate with MLOps Engineers on the integration of Pruna's solutions in proprietary and established cloud platforms • Qualify new partnerships, experiment together on new tools and offering for the ML community to generate leads and convert customers

• Design, develop, and deploy AI/ML solutions to modernize core pharmacy platforms, with a focus on scalability, reliability, performance, and security • Leverage Generative AI, LLMs, and agentic AI frameworks to automate and enhance pharmacy workflows and decision-making processes • Collaborate with business and technical stakeholders to understand pharmacy domain requirements and translate them into robust AI/ML-driven technical solutions • Contribute to architecture and technical design of AI/ML pipelines, including model selection, data integration, and deployment patterns • Establish and maintain AI/ML engineering standards, best practices, and quality assurance processes • Conduct code reviews, provide constructive feedback, and mentor engineers on modern AI/ML engineering practices • Partner with cross-functional teams including data scientists, product managers, platform engineers, and pharmacy domain experts • Translate legacy system modernization needs into scalable AI applications that enhance products, workflows, and operational efficiency • Monitor and optimize AI/ML model performance, resource utilization, and platform reliability • Collaborate with DevOps and infrastructure teams to ensure secure, scalable deployment and operations of AI/ML solutions • Stay current with advancements in AI/ML frameworks, Generative AI, LLMs, and pharmacy technology modernization

• Support balancing and simulation work across core game systems: heroes, troops, economy, progression, and events • Help improve and operate our AI-assisted balancing toolchain and workflows • Build scripts, automations, and ML-supported workflows to improve balancing efficiency and iteration speed • Analyze balancing data, simulation outputs, and player behavior signals to support balancing decisions • Work closely with game design, QA, engineering, and production on balancing topics and tooling improvements • Help document and structure balancing processes, prompts, datasets, and workflows for scalable AI-assisted production

• Build and maintain large-scale distributed training and evaluation pipelines for Deep Reinforcement Learning. • Design scalable infrastructure for training, evaluation, model management, and experiment tracking. • Build dashboards and monitoring tools to track training progress, model quality, compute usage, and agent performance. • Optimize the training and inference performance of our Deep Learning models. • Improve cost efficiency across cloud/GPU infrastructure and make high-impact infrastructure decisions. • Work closely with researchers and engineers to reduce iteration time and improve model accuracy. • Help design reproducible ML workflows, including data pipelines, checkpointing, evaluation, versioning, and deployment. • Identify bottlenecks across the full ML stack: model architecture, data loading, GPU utilization, distributed training, inference, and infrastructure. • Contribute directly to ML improvements that increase accuracy, robustness, and compute efficiency.