Job Description
Integrations Engineer 800 Brickell Avenue, Miami, FL 33131 $120,000 a year - Full-time $120,000 a year - Full-time Date posted: May 22, 2026
Pay:
$120,000.00 per year Job description: Integrations Engineer at Jemm Tec TO START IMMEDIATELY.
Compensation:
$120,000 per year Location:
In-person required (Miami, FL) Reports to: General Manager Team:
5 - 10 engineers on the Jemm Arc product Workstation:
Jemm Tec MacBook About Jemm Tec Jemm Tec LLC (jemm.ai) builds the Jemm Arc product across hardware and software. We run on private servers, you will be given a company laptop and operate a five + layer platform stack. Our infrastructure of record lives in Jira, Confluence, and GitLab at this time. The role This is a software development role. You will build the software platform that sits between the Artificial Intelligence Engineer and the Designer at Jemm Tec, and you will be the engineer who ships the functional logic and backend systems that make their work meet inside a real product. The integrations aspect of the role: On one side, the Artificial Intelligence Engineer builds and trains the custom LLM that powers the Jemm Arc product. They own model architecture, training data, fine tunes, evaluation, and the model artifacts that come out the other end. On the other side, the Designer covers two domains. The first is the physical hardware design of the Jemm Arc itself (CAD, mechanical, enclosure, board layout thereof). The second is the web design of the user facing surfaces (mobile app, web admin console, on device UI, public product pages thereof). You sit between them and build the software platform itself. The LLM the AI Engineer trains gets injected into the platform you build. The designs the Designer produces get rendered on the user facing side of the platform you build. Your work is the functional logic, the backend systems, the API layer, the runtime that holds the model, the data plumbing, the device side software, and the companion app code thereof. Without the platform you build, the model has nowhere to live and the design has nothing to drive. Concretely, you make sure a hardware revision drawn in Fusion has the I/O the inference module needs. You make sure a mobile or web screen drawn in Figma actually calls the inference gateway with the right schema. You make sure the trained model the AI Engineer hands you runs on the device with the latency budget the product requires thereof. Without this seat, design handoffs queue up behind the AI Engineer. The AI Engineer has to context switch between training the model and wiring its outputs into firmware and product UI. The Designer's work either ships late or ships compromised because no one is the connective tissue. You are the connective tissue. You will join as one of the 5-10 engineers, report to the General Manager, and operate Claude Code (run through Cursor) as your daily AI copilot, with DeepSeek v4 available as the secondary model where it fits the task better. What we are looking for:
On device AI deployment experience. This includes quantization (int8, int4, GGUF, AWQ thereof), edge inference runtimes (llama.cpp, ONNX Runtime, Core ML, ExecuTorch, TensorRT, &c.), and latency profiling on constrained hardware. Full stack Go code fluency. You will write the backend services, the device runtime, the inference gateway, the data plumbing, and the platform layer the LLM is injected into. API contract design (OpenAPI or gRPC). Comfortable drafting a spec, getting it reviewed, then implementing both sides thereof. Fluent in reading design tooling output. Figma for product, Fusion or Onshape or KiCad for hardware. You do not need to design. You need to read designs accurately and turn them into engineering work thereof. Comfortable steering an AI copilot (Claude Code inside Cursor, DeepSeek v4 inside Cursor). Nice to have:
OTA update infrastructure experience (system A/B partitions, rollback paths, signed updates thereof) BLE, MQTT, or other device to cloud protocol experience at production scale Prior work in regulated hardware contexts (FCC, CE, UL filings touched by firmware changes) Experience with a model registry (MLflow, Weights and Biases) on the consumption side. You will not train, but you will pull and ship. Fluent on macOS. We run on macOS engineering workstations and private macOS servers thereof. Familiarity with WebGL or WebGPU surfaces (Three.js, React Three Fiber). Useful for the companion app's hardware visualization views. Benefits:
401(k) Dental insurance Flexible schedule Health insurance Retirement plan Vision insurance Work Location:
In person Pay:
$120,000.00 per year Benefits:
401(k) Dental insurance Flexible schedule Health insurance Retirement plan Vision insurance Work Location:
In person
