Job Description
Krane is building intelligent tools that power the future of construction operations. You’ll lead the design and deployment of intelligent systems that automate project documentation, streamline supplier workflows, and unlock new efficiencies across the construction supply chain.
You’ll work at the intersection of machine learning, product, and operations—turning messy, real-world construction data into reliable, scalable features that directly impact customers.
Responsibilities
- Design, build, and deploy ML models for document parsing, anomaly detection, forecasting, and decision support across construction workflows.
- Leverage LLMs, NLP, OCR, and embeddings to process unstructured data such as RFQs, invoices, delivery notes, emails, and project documentation.
- Develop end-to-end ML pipelines for data ingestion, feature engineering, model training, evaluation, and deployment to production.
- Collaborate with product and engineering teams to ship ML-powered features—from ideation and prototyping through to launch and iteration.
- Implement robust evaluation and monitoring for models in production, including performance tracking, drift detection, and error analysis.
- Optimize models and inference systems for latency, cost, and reliability in real-world, production environments.
- Work with structured and unstructured construction data to uncover patterns, identify anomalies, and surface actionable insights to users.
- Contribute to Krane’s AI strategy, helping define technical direction, best practices, and standards for ML across the engineering organization.
- Document your work clearly, including model assumptions, limitations, and operational runbooks for production systems.
- Stay current with modern AI / ML tooling and research and evaluate emerging methods that can improve Krane’s product and platform.
What We’re Looking For
3–4 years of experience in AI / ML, applied machine learning, or data science roles.Strong Python skills and experience with ML frameworks such as PyTorch or TensorFlow , plus common libraries (e.g., scikit-learn, Hugging Face, OpenCV, spaCy).Hands-on experience with production ML systems, including containerization (e.g., Docker), APIs, and cloud-based deployment (AWS, GCP, or Azure).Experience with NLP / LLMs and unstructured data, including tasks like classification, extraction, semantic search, and text generation.Familiarity with building scalable data & ML infrastructure, such as feature pipelines, model registries, and batch / streaming workflows.Solid understanding of ML fundamentals : model selection, evaluation metrics, cross-validation, bias / variance, and experimentation.Comfort in a product-focused environment, working closely with designers, engineers, and product managers to balance impact, feasibility, and speed.Strong communication skills and the ability to explain complex ML concepts to non-technical stakeholders.High ownership and a builder mindset —you’re excited to help define how AI / ML is done at Krane from the ground up.Bonus
Experience in construction, logistics, manufacturing, or other physical / operations -heavy industries.Experience with B2B SaaS products or internal operations tools.Familiarity with MLOps platforms and observability tools (e.g., MLflow, Weights & Biases, KServe, Seldon, etc.).Compensation
Base Salary : CompetitiveEquity : YesBenefits : Competitive benefits package and Flexible PTO