Full Stack Engineer

Location: Remote. Candidates based in Latin America, India, or the United States are welcome to apply.

Compensation: $2000-$2500 per month Time Commitment: ~15 hours per week; reasonable availability during US Central Time zone; flexible work-hours

Position Type: Independent contractor

Duration: nine-month engagement, with the intention to extend based on mutual fit and company needs About Nexa Labs (part of Spade) We're building smart monitoring devices for cows - small, low-power wearable and implantable devices that monitor animal health and behavior on real farms. Our mission is to use technology to empower farmers and the food system that they drive.

About the role: We're hiring a Full-Stack Engineer to own the software systems connecting our devices in the field to the applications used by farmers and internal teams. This role spans backend services, data pipelines, frontend applications, edge connectivity, cloud infrastructure, and applied data science. You will work closely with an electronics/firmware lead responsible for device hardware and firmware development, in addition to diverse stakeholders spanning engineering, product, and customers. Your primary responsibility will be building and operating the software, infrastructure, and data systems surrounding the devices. We're looking for a versatile engineer who can move across the complete software stack, rapidly build and deploy systems, and take ownership wherever needed.

What you'll do: Own end-to-end software architecture and development across edge gateways, connectivity, cloud infrastructure, backend services, databases, frontend applications, and machine-learning pipelines Develop and maintain device-to-cloud data pipelines, backend services, APIs, and data models using Python, Django, relational databases, and related technologies Develop and maintain edge and LoRa/LoRaWAN systems, including device communications and gateway integration Develop and maintain customer-facing and internal applications using React and TypeScript Develop and maintain cloud infrastructure, device-management systems, and remote diagnostics, alerting using AWS, GCP, Terraform, or similar tools Develop and deploy practical machine-learning models and analytics using sensor and time-series data, including data preparation, feature engineering, training, evaluation, and production integration Collaborate with other engineers to define device interfaces, telemetry formats, provisioning workflows, diagnostic capabilities, and update processes Work closely with hardware, data science, veterinary, manufacturing, and farm-facing teams on product development and field deployment Support pilot deployments, investigate failures across the software stack, and rapidly implement improvements based on field data and user feedback You should have 3+ years of professional experience building and shipping IoT, distributed, full-stack, and/or data-driven software systems Strong programming ability in Python and TypeScript/JavaScript, with experience using modern frameworks such as React and Django Strong depth and experience across several areas of connected-product development, such as edge software, backend systems, cloud infrastructure, data pipelines, databases, and full-stack applications Experience building device-to-cloud integrations and designing reliable systems around connected devices Experience with cloud platforms, CI/CD, infrastructure as code, monitoring, logging, and production debugging Demonstrated ability to quickly learn unfamiliar technologies and contribute outside a primary area of expertise Drive to use AI tools to accelerate engineering Comfort with ambiguity, fast iteration, and startup pace

Nice to have: Experience building agricultural, industrial, robotics, wearable, medical-device, or other field-deployed connected systems Familiarity with embedded firmware development and C/C++ Experience with LoRaWAN network servers such as ChirpStack, The Things Stack, or equivalent systems Experience with time-series databases, stream processing, or event-driven architectures Experience integrating machine-learning models or algorithms into production data pipelines