Job Post Template: Edge AI Engineer (Raspberry Pi & On-Device Models)

Hire an Edge AI Engineer who can ship on-device generative AI (Pi 5 + AI HAT+): ready-to-use job post & interview scorecard

Hook: You need engineers who can deploy on-device generative AI quickly — not theory-only ML people. The market in 2026 rewards teams that can run large language and multimodal models locally (Raspberry Pi 5 + AI HAT+), keep inference costs down, and ship secure, private micro apps. This guide gives you a complete, copy-paste job post, a robust interview scorecard, sample take-home tasks, and salary guidance tailored for hiring embedded/edge ML engineers.

The 2026 context: why Raspberry Pi 5 + AI HAT+ matters now

Late 2024–2026 saw a rapid shift: inference stacks and quantized model families matured, low-cost hardware like the Raspberry Pi 5 paired with affordable accelerators such as the AI HAT+ unlocked practical on-device generative AI for micro apps, privacy-first browsers, and industrial endpoints. Non-developers building "micro apps" and local AI browser projects accelerated demand for small, private LLM deployments. Hiring for embedded ML now means evaluating proficiency in model compression, runtime engineering, and power/thermal tradeoffs — not just PyTorch notebooks.

On-device AI is now a product differentiator: lower latency, better privacy, and cheaper long-term TCO for high-volume or offline scenarios.

What top-of-stack Edge AI candidates must be able to do (skills checklist)

• Embedded systems: Linux on ARM (Raspberry Pi 5), cross-compilation, kernel modules, device trees.
• Model optimization: quantization (INT8/4-bit), pruning, LoRA, QAT basics, and familiarity with tools like MLC-LLM, TVM, ONNX Runtime, TensorFlow Lite.
• On-device inference: run optimized transformer models (2B–13B compressed) on Pi5+AI HAT+, measure latency, and minimize memory overhead; see field patterns for local-first sync appliances and privacy-friendly inference.
• Systems integration: sensor I/O, audio pipelines, camera interfacing, batching, and edge gateways.
• Security & privacy: secure boot basics, encrypted model storage, inference request audit logs, GDPR/CCPA awareness.
• Benchmarks & observability: end-to-end benchmarking (latency, throughput, power), and lightweight telemetry on constrained devices.
• Software engineering: CI for embedded builds, reproducible Docker/Yocto workflows, unit tests for edge code.

Ready-to-use job post: Edge AI Engineer (Raspberry Pi 5 + AI HAT+)

Copy and paste, then customize for company tone, benefits, and location.

Job summary (copy-paste)

We are hiring an experienced Edge AI Engineer to design, optimize, and ship on-device generative AI solutions running on Raspberry Pi 5 paired with AI HAT+ accelerators. You will move models from research to constrained production environments, own inference performance and reliability, and collaborate with product and backend teams to deliver private, low-latency micro apps.

Responsibilities

• Deploy and optimize transformer-based models for on-device inference on Raspberry Pi 5 + AI HAT+ (quantization, compilation, runtime tuning).
• Build reproducible build pipelines for embedded Linux targets (cross-compilation, Yocto, Docker).
• Implement lightweight model serving and telemetry for constrained devices (onnxruntime, mlc-llm, tflite, micro-runtimes); consider local-first sync patterns from local-first appliances.
• Design and run benchmarks: latency, memory, CPU/GPU/accelerator utilization, and power draw.
• Integrate on-device models with sensors, audio, and camera pipelines as required by product features.
• Ensure secure storage of models and safe inference behavior (content filters, local moderation heuristics); tie into edge storage patterns for small SaaS.
• Document deployment patterns and create templates so other teams can replicate work.

Must-have (minimum qualifications)

• 3+ years building embedded or edge ML solutions (production experience required).
• Hands-on experience with Raspberry Pi (RPi4 / RPi5) or similar ARM boards and accelerator HATs.
• Proven track record optimizing transformer models for constrained hardware (quantization, pruning, LoRA).
• Familiarity with one or more runtimes: ONNX Runtime, MLC-LLM, TensorFlow Lite, TVM.
• Strong Linux, shell scripting, and build system skills; experience with cross-compilation/Yocto is a plus.
• Comfortable writing production-quality code in Python and C/C++.

Nice-to-have

• Experience with audio and vision pipelines, ROS, or edge robotics integrations.
• Experience with secure boot, TPM, or encrypted model storage.
• Past work shipping micro apps or local AI browser integrations.

Hiring process

1. Initial recruiter screen (30 min).
2. Technical phone screen: ARM/Linux, model optimization (45 min).
3. Take-home deployment task (4–8 hours) — candidate delivers code + short report. Consider offering a loaner device or references to community testbeds like Pi5+LLM guides.
4. On-site / live coding & demo: system design and live deploy to Pi5+AI HAT+ (2–3 hours split across days).
5. Reference checks and offer.

Salary & compensation (guidance)

2026 market rates vary widely by location and remote parity. Below are starting ranges; adjust for company size, funding, and equity.

• US (Remote, High-cost regions): Mid: $130k–$170k; Senior: $170k–$230k total comp.
• US (Non-coastal): Mid: $110k–$150k; Senior: $150k–$190k.
• EMEA (UK/EU): Mid: €80k–€120k; Senior: €120k–€170k.
• LatAm: Mid: $35k–$70k; Senior: $70k–$110k (local cost adjustments).
• Asia (India): Mid: $20k–$45k; Senior: $45k–$90k.
• Contract / Consulting: $80–$240/hr depending on expertise and deliverables (short-term, on-site demo rates higher).

Why these bands in 2026? Scarcity of engineers with true on-device generative AI experience + rising demand from privacy-first products and edge-first vendors pushed rates up in late 2024–2026. Remote parity and equity packages remain common for senior hires.

Interview scorecard: objective rubric for Edge AI engineers

Use a consistent scorecard to reduce bias and increase signal-to-noise. Score each area 1–5; multiply by weight to get a subtotal. Passing threshold: 70/100 or higher, with no area below 2 if role-critical.

Scorecard categories & weights

• Technical deployment task — 40%: real-world take-home that includes model quantization + deploy to Pi5+AI HAT+, deliverables: code, README, benchmark results. See community writeups like the Pi5 pocket inference guides for reproducible approaches (run local LLMs on Pi5).
• System design & architecture — 25%: on-site discussion of trade-offs, failure modes, telemetry, and scaling; include OTA and storage patterns from edge storage.
• On-device demo / live troubleshooting — 20%: live or recorded short demo showing model boot, inference, and tuning steps; judge debugging speed. Consider field notes from local-first appliances to evaluate privacy and sync behavior.
• Code quality & maintainability — 10%: clarity, tests, reproducibility of the take-home repo.
• Communication & team fit — 5%: clarity of documentation, stakeholder alignment skills.

Scoring scale (1–5)

• 5 — Exceptional: deep, production-ready knowledge and can mentor others.
• 4 — Strong: solid production experience, handles complexity well.
• 3 — Competent: can complete typical projects with some guidance.
• 2 — Weak: requires substantial mentoring to be productive.
• 1 — Not acceptable: gaps in basic required skills.

Sample evaluation rubric (use as a checklist)

• Technical take-home: Correctly quantized model, deterministic build instructions, latency & memory benchmarks, explainable regressions.
• System design: Thoughtful failure handling (power loss, thermal throttling), OTA update plan, telemetry design with low-data footprint.
• Demo: Bootstraps model in < 90s, inference latency meets stated target, shows debugging steps for a common fault.
• Code: README with reproducible steps, CI or reproducible Docker/VM, tests or smoke checks.
• Communication: Clear documentation for product stakeholders and a realistic deployment timeline.

Sample take-home tasks (practical, time-boxed)

Design take-homes so they are evaluative of production skills but respect candidate time (4–8 hours recommended). Provide hardware access remotely if possible (e.g., loan a Pi5+AI HAT+), or allow emulation where appropriate.

Task A — Quantize & deploy a small LLM (recommended)

Deliver a repo that downloads a publicly available model (<=7B), applies 4-bit quantization or a supported compression method, and deploys a text generation endpoint on Raspberry Pi 5 + AI HAT+ or a provided emulation image. Include:

• Scripts to build the runtime (Dockerfile or Yocto snippet) and run inference.
• Benchmark report: cold start, steady-state 1-token latency, peak memory use, and power draw (if hardware available).
• Notes on trade-offs: accuracy loss vs. token throughput, any LoRA or QAT steps attempted, and mitigation ideas.

Evaluation tips: Accept ORT/MLC-LLM/TFLite implementations. Look for reproducible results and thoughtful trade-off analysis. Field guides on running local LLMs and local-first sync patterns are good references (Pi5 LLM guide, local-first appliances).

Task B — Edge app micro-demo (optional, targeted)

Implement a tiny end-to-end micro app that accepts audio or text and performs an on-device generation or classification. Deliver a short video (3–5 minutes) showing the device in action plus code. This shows integration ability: audio preprocessing, batching, and response sanitization.

Time & deliverables

• Timebox: 4–8 hours for core task A; 2–4 hours for optional task B.
• Deliverables: Git repo, README, benchmark CSV or JSON, short write-up (1–2 pages) on tradeoffs and next steps.

Live interview & system design prompts (examples)

Run a 45–90 minute session focused on real constraints.

• Design a 10k-device fleet of on-device LLM gateways that must update models securely and report telemetry without exceeding 1KB/day/device. Sketch OTA, authentication, and rollback strategies; consider edge storage and local sync approaches.
• Explain how you'd reduce 1-token latency from 120ms to 50ms on Pi5+AI HAT+. What layers would you optimize? Which trade-offs hurt accuracy the least? Look for answers referencing runtime tuning and lightweight observability (latency & observability).
• Given a 7B model that exceeds RAM, describe a strategy using quantization + paging or offloading to an accelerometer without violating privacy rules.

Offer letter snippet & onboarding checklist (copy-paste)

Offer snippet

[Date]
Dear [Candidate Name],
We are pleased to offer you the role of Edge AI Engineer at [Company]. Your starting salary will be [Salary], with [equity/options] and standard benefits. Your start date will be [date]. This offer is contingent on reference checks and signed employment agreement covering IP and confidentiality. We look forward to your contributions to our on-device AI initiatives.

Onboarding checklist (first 30 days)

• Provision Raspberry Pi 5 + AI HAT+ device (company-supplied or reimbursement if remote).
• Access to CI/CD and embedded-build servers, credentials for model registry; tie storage rules into edge storage guidance.
• First-week task: reproduce a baseline inference from the team’s canonical repo and present findings.
• First 30-day deliverable: ship a performance improvement or reliability bug fix to the device baseline.

Salary guidance & negotiation tips for 2026

When setting salary bands in 2026, consider these market signals:

• Supply shortage: Engineers who can reliably compress and ship generative models on small hardware are scarce; be prepared to pay a premium for senior hires and to offer flexible remote or hybrid options.
• Location flexibility: If you hire remote, use regional benchmarks and a transparency model (base + location adjustment + equity).
• Contractor option: For short needs (proof-of-concept or product kickoff) consider a 3-month contractor to validate product-market fit before committing to full-time headcount.

Negotiation tips:

1. Quote a competitive band, not a single number. Example: $150k–$180k for a senior remote hire in the US.
2. Offer fast, clear decision timelines — candidates with niche skills expect speed.
3. Bundle training & hardware allowances if candidates need to upskill on your stack.

Screening for scams and verifying candidate claims

Vetting edge-ML candidates requires more than résumé checks. Use these practical steps:

• Ask for a reproducible demo or recorded walkthrough on real hardware or verified cloud images; community guides for running local LLMs on Pi5 are useful references (Pi5 LLM guide).
• Verify open-source contributions and challenge tasks against known public repos to ensure originality.
• Use reference checks focused on production outcomes: uptime, rollout issues, and team handoffs.

Advanced hiring strategies & future-proof attributes

Beyond immediate skills, prioritize candidates who show:

• Experience with model compilers and graph-level optimization (TVM, Glow, XLA).
• Ability to design for observability in low-bandwidth environments and to implement model feedback loops without PII leakage; local-first sync appliances are a good pattern to study (local-first sync appliances).
• Track record of transferring research prototypes to reproducible product artifacts — not just papers or demos.
• Curiosity about privacy-preserving techniques (federated learning, differential privacy) as these become critical for constrained-device fleets.

Example (short) case scenario — how one hire accelerates product

Scenario: A small product team hires a senior edge engineer with Raspberry Pi 5 + AI HAT+ experience. Within 8 weeks they ship a local-first summarization feature for offline users. The engineer implemented model quantization, reduced cold-start time by 60%, and created a CI pipeline for OTA updates. The result: faster adoption among privacy-conscious customers and 40% lower inference costs compared to cloud-only options.

Use such outcome-based interviews to evaluate candidates: ask them to walk through a specific shipped feature and the measurable impact.

Quick checklist for posting & screening this role

• Post the job with the copy-paste template above; specify hardware requirement and whether the company will supply devices.
• Require a short portfolio link and a short answer: "Describe one quantization or model-size trade-off you made in production."
• Use the scorecard consistently across interviews.
• Timebox take-home tasks and provide clear evaluation criteria.

Final takeaways

Hiring for edge AI in 2026 demands a practical, outcome-focused approach. Prioritize candidates who can move models from prototype to resilient on-device services: reproducible build artifacts, measured benchmarks, and secure deployment plans. Use the job template, scorecard, and sample tasks in this guide to reduce hiring time, increase hire quality, and lower the risk of mismatched expectations.

Ready to hire? Post this job now, run the take-home task, and use the scorecard for consistent decisions — your first on-device generator could be shipping in weeks, not months.

Call-to-action

Post this role today on onlinejobs.website or contact our hiring team for tailored candidate sourcing, screening services, and Pi5 + AI HAT+ device loans for vetted candidates. Accelerate hiring and reduce time-to-production for your on-device AI products.

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