Applied AI/ML Scientist

Beamup builds AI agents for supply chain and operations at scale. Our agents find and fix failures inside large supply chain and operations businesses: retailers, marketplaces, fulfillment operators and manufacturers. They cut through the noise in the fifteen to twenty systems our customers already run, find the failures that matter, prove what they cost, and fix the cause autonomously so it stops repeating, before the end customer ever finds out.

We are deployed in more than twenty countries, hold eleven granted AI patents, and count Fortune-100 customers. 

To close the gap between what every customer-facing team hears and what every operations team sees. So delivery promises hold, customer loyalty compounds, and nobody starts from zero the next time the same failure comes back.

We are looking for an experienced Applied AI/ML Scientist with expertise in building agentic systems and autonomous agents to join one of our R&D. You will be at the core of transforming our supply chain solutions into a fully agentic platform, designing and building agents that autonomously generate analytical pipelines, orchestrate multi-step reasoning, and resolve complex logistics challenges for our customers. You will combine strong machine learning and deep learning expertise with the ability to architect and implement production-grade agentic systems, working closely with engineering, product, and domain experts to push the boundaries of what autonomous AI can do in supply chain.

• →Design and build autonomous agentic systems that generate, configure, and execute analytical pipelines to solve supply chain challenges end-to-end
• →Architect multi-agent workflows with planning, tool use, memory, and feedback loops, enabling agents to reason, adapt, and improve over time
• →Develop and integrate ML and deep learning models (e.g., predictive models, anomaly detection, demand forecasting) as core capabilities within agentic pipelines
• →Research and apply state-of-the-art techniques in agentic AI, LLM orchestration, and multi-agent systems to production use cases
• →Translate complex logistics and supply chain challenges into agent-based problem formulations, collaborating closely with product and domain experts
• →Define and implement rigorous evaluation frameworks for agent performance: correctness, reliability, robustness, and edge-case handling
• →Collaborate with software engineers to productize agentic solutions — including testing, monitoring, versioning, and iterative improvement
• →Contribute to team practices: reproducible code, experiment tracking, documentation, and knowledge sharing

• 4+ years of experience in applied data science or ML in a product environment, with demonstrated experience building agentic systems or autonomous agents
• MSc in Computer Science, Data Science, Mathematics, Statistics, Engineering, or a related field (or equivalent practical experience)
• Proven track record designing and implementing multi-step agentic pipelines, including LLM-based agents, tool use, planning loops, and memory mechanisms
• Hands-on experience with agentic frameworks such as LangChain, LangGraph, AutoGen, or equivalent
• Strong Python coding skills; familiar with Spark for large-scale, distributed data processing
• Experience with LLM APIs (e.g., OpenAI, Anthropic, Bedrock, open-source models) and prompt engineering for agentic use cases
• Experience performing rigorous model evaluation (baselines, cross-validation, error analysis) and defining evaluation strategies for agent behavior
• Strong communication and collaboration skills; able to work across engineering, product, and supply chain domain experts and iterate fast

• Experience with multi-agent architectures, agent-to-agent communication protocols, and agent orchestration at scale
• Experience with cloud platforms (AWS, GCP, Azure) and MLOps practices (CI/CD for ML, model monitoring, drift detection)
• Familiarity with containerization and production engineering practices (Docker, Kubernetes)