Top Tech Jobs & Startup Jobs

Support and own day-to-day operational workflows across finance, legal, people operations, vendor management, and internal systems. Build analyses, financial models, dashboards, and decision materials for leadership. Coordinate cross-functional projects, document processes, create lightweight automations, maintain core systems (Notion, Justworks, Google Drive), and drive high-priority projects to completion with strong judgment and attention to detail.

Work directly with the CEO to multiply their effectiveness by managing external relationships, driving cross-functional strategic initiatives, preparing briefs and board materials, representing the CEO externally, running executive operating rhythms, and ensuring follow-through across sales, operations, engineering, and leadership.

Own full-cycle recruiting for Hardware and Robotics roles; partner with hiring managers to define requirements; proactively source passive candidates; build pipelines across electrical, embedded, firmware, mechanical, perception, and systems disciplines; drive excellent candidate experience; evolve interview and offer practices; represent Treeswift and sell the hardware vision.

Lead deployments and expansion at strategic utility customers: train users, define project requirements, track deliveries, present results to executives, and identify growth opportunities. Act as the customer-facing product partner, translating field needs into product features and collaborating closely with engineering and product teams.

Lead and scale the platform that schedules pipelines, runs ML training/inference, and hosts the web app. Build reliability and observability for Airflow/Astronomer DAGs, Kubernetes/ECS workers, and AWS services. Own CI/CD guardrails, incident runbooks, operational tooling, and automate toil while partnering with data and engineering teams to ensure production readiness and safe rollouts.

Lead design and implementation of reliability, observability, and CI/CD for Airflow/Astronomer pipelines and containerized workloads on AWS/Kubernetes. Build monitoring, alerting, SLOs, inference instrumentation, operational tooling, runbooks, and automate toil while partnering with data and engineering teams to scale production ML and data pipelines.