Applied AI/ML Scientist

About Us

Beamup helps enterprises move beyond dashboards, alerts, and manual triage to automated supply chain execution.

Our AI platform supports large, complex retail and manufacturing networks by deploying specialized AI agents that operate continuously across stores, distribution centers, warehouses, and in-transit operations. These agents detect execution issues, identify root causes, and execute corrective actions - either autonomously or by routing work to the right teams.

Retailers and manufacturers rely on Beamup to replace reactive workflows with consistent, scalable execution — reducing losses, improving performance, and operating with greater confidence at global scale.

Our Mission

To redefine supply chain intelligence by building AI agents that predict, prevent, and resolve inventory health and operational issues in real time.

Job Summary:

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.

Responsibilities:

• 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)