• Build core ML systems for applications • Own work from data prep to iteration • Collaborate with teams to impact user experience • Debug production issues and improve systems

• Build and improve ML components across data, training, evaluation, and inference. • Fine-tune and adapt models as part of larger production systems. • Implement evaluation and testing to understand model behavior. • Help build and maintain data pipelines for real-world and synthetic data. • Debug model issues, performance problems, and production incidents. • Ship improvements iteratively and learn from real user feedback. • Work closely with senior ML engineers and product teams. • Work under real production constraints: latency, cost, reliability, and safety

• Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment. • Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation. • Architect and operate scalable inference systems, balancing latency, cost, and reliability. • Design and maintain data systems for high-quality synthetic and real-world training data. • Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. • Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies. • Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products. • Make pragmatic trade-offs and ship improvements quickly, learning from real usage. • Work under real production constraints: latency, cost, reliability, and safety

• Set and evolve the research direction for A1’s core intelligence, including context representation, memory, reasoning, planning, and orchestration. • Decide when to design new model architectures versus adapting or leveraging frontier open-source or commercial models. • Define evaluation frameworks that measure real-world usefulness, robustness, safety, and long-term behavior – not benchmark vanity. • Own alignment, safety, and guardrail strategy as first-class product concerns. • Guide exploration of frontier techniques such as: • retrieval-augmented training • mixture-of-experts • distillation • multi-agent orchestration • multimodal systems • Shape early product intelligence direction in close partnership with product and application engineering. • Set the technical bar for research rigor, judgment, and taste across the organization.