Software Engineer

Software Engineer – Data & PlatformLocation: Pittsburgh, PA (on-site)

Compensation

• $120,000 – $150,000Why Curve
• Point
• Curve
• Point uses wireless signals to sense human presence and movement in real time, with no cameras.
• We deploy this in schools, where the systems have to run continuously, respond within tight latency budgets, and stay reliable on constrained, unpredictable networks.
• There is very little room for error, because the outcome is real: when these systems work, they help prevent violence and save lives.

The work spans edge systems, networking, data infrastructure, and machine learning. If you want problems where correctness genuinely matters and the stakes are high, this is that role.

What You’ll DoYou'll build the core software behind CurvePoint's Wi-AI sensing platform: the systems that move data from real-world devices into production ML pipelines, reliably and at scale. The role blends backend development, data engineering, and edge/IoT systems, and you'll own that path end to end.

This is a hands-on role for someone who likes shipping, debugging, and owning systems, not just contributing to them.

Responsibilities

• Build and operate backend services and APIs for device management, data ingestion, and analytics.
• Design and maintain data pipelines for high-volume, time-series sensor data.
• Develop software that runs on edge and embedded Linux devices: buffering, health checks, sync, and monitoring.
• Own edge-to-cloud workflows across unreliable networks and constrained environments.
• Deploy and operate containerized services in AWS.Work directly with ML and hardware engineers to turn research and prototypes into production systems.
• Must Haves3+ years of professional software engineering experience.
• Strong Python and fluency in Linux environments.
• Experience building backend services, APIs, and data pipelines.
• Solid grasp of relational databases (PostgreSQL a plus), data modeling, and distributed systems fundamentals.
• Experience deploying and operating software in the cloud (AWS preferred).
• Care for data quality and correctness, and a habit of debugging production issues and owning them.
• Nice to Have
• Experience with data-quality tooling, validation frameworks, or pipeline orchestration (Airflow, Dagster, or similar).
• Familiarity with streaming, queues, or event-driven architectures.
• Front-end or full-stack experience for building internal tools and dashboards (React or similar).
• Exposure to ML pipelines or data-heavy systems.