Top Tech Jobs & Startup Jobs

Build and maintain Extensions backend for enterprise LTM products: design distributed, reproducible, observable workflows; optimize latency and throughput; collaborate with data scientists; drive architectural decisions; write high-quality, tested Python code; and document best practices for production-scale deployments.

Build and maintain front-end and back-end systems for model interaction and large-scale inference. Develop APIs, session persistence, event-driven architectures, database integrations (Snowflake/Databricks), and monitoring/retry mechanisms. Collaborate with MLOps/DevOps to optimize deployment and ensure reliable, scalable model-serving workflows.

Work with enterprise customers to identify high-value tabular ML use cases, design production-grade architectures, prototype solutions, lead deployments, coordinate cross-functional teams, and codify reusable deployment patterns to scale adoption of the NEXUS tabular foundation model.

Research, develop, and productize data-science methods to improve NEXUS predictive performance on enterprise tabular datasets. Implement production-quality Python components, design experiments and benchmarks, handle real-world tabular data challenges, collaborate with Research and Engineering, and validate approaches on customer datasets to drive product features.

Design, build, and operate cloud infrastructure and Kubernetes clusters for GPU/ML workloads; implement GitOps, IaC (Terraform), CI/CD, monitoring, automation (Python/Bash), and cost-optimization alongside ML engineers.

Deploy and validate NEXUS in customer environments, perform end-to-end data and feature engineering, benchmark against baselines (XGBoost/LightGBM), optimize models for business impact, handle last-mile integrations (VPC/on‑prem/air-gapped), and translate field insights into product improvements while liaising with stakeholders.