Jobs at Robots & Pencils

Train and operate machine learning models on Amazon SageMaker using AWS Trainium. Develop and optimize PyTorch training code for NeuronCore hardware, diagnose device and compiler issues, tune distributed workloads for throughput and cost, and deliver production training pipelines while collaborating directly with client and engineering teams.

Leads end-to-end design, development, and deployment of scalable AI/ML systems. Defines architecture and engineering standards, builds ML platforms and RAG pipelines, optimizes models and costs, and ensures reliability, security, observability, and responsible AI practices. Partners with product and leadership, communicates technical tradeoffs, leads design reviews, mentors engineers, and delivers production agentic systems using cloud, containerization, and AI-forward development tools.

Leads the architecture, development, optimization, and production deployment of complex AI and machine learning systems. Builds scalable ML platforms, pipelines, and infrastructure; establishes engineering standards; guides responsible AI practices; and mentors engineers. Partners with product and leadership teams to shape AI strategy, communicates technical tradeoffs, and drives high-stakes initiatives focused on reliability, scalability, security, cost, and safety. The role requires deep generative AI, agentic systems, RAG, cloud, and production engineering expertise.

Lead the architecture, development, deployment, and adoption of production AI/ML systems within client environments. Build scalable ML platforms, agentic systems, RAG pipelines, and integrations; resolve complex technical challenges; establish responsible AI and engineering standards; communicate tradeoffs to stakeholders; support client enablement; and mentor engineers. The role requires deep expertise in generative AI, AWS, cloud infrastructure, observability, security, cost optimization, Docker, and Kubernetes.

Leads the technical vision and delivery of an enterprise agentic AI program. Oversees architecture, evaluation, deployment, MLOps, reliability, security, and scalability across multiple workstreams. Partners with executives and client stakeholders on roadmaps and high-stakes decisions, aligns data science and engineering teams, establishes technical standards, and mentors senior technical leaders. Requires deep experience in AI/ML, LLMs, agentic systems, AWS, distributed systems, evaluation, and production-scale software engineering.

Train and operate production machine learning models on Amazon SageMaker using AWS Trainium. Optimize PyTorch code for Trainium compilation, memory, throughput, and distributed training; diagnose hardware, compiler, device, data, and code issues; build cost-aware end-to-end training pipelines; and collaborate directly with client and internal engineering teams to deliver production workloads.