We’re hiring an Android Applications Developer (Contractor) to design, build, and continuously evolve production‑grade Android/Android Application Developer (Applied ML) applications and services.
Responsibilities
- Integrate prebuilt ML models (e.g., TFLite, MediaPipe, ONNX Runtime) into Android/AAOS apps and services with clear and maintainable model I/O contracts.
- Offline‑first inference; optional cloud inference path (REST/gRPC) using the hosted model endpoint; implement routing and fallback between local and cloud paths.
- Develop robust, real‑time audio and sensor capture pipelines (AudioRecord/AAOS audio APIs; optional car audio plugin service) with buffering and back‑pressure handling.
- Implement model pre/post‑processing as specified by the ML team (e.g., windowing, normalization, log‑mel) using provided reference code and evolving best practices.
- Design and maintain event logic (thresholds, debouncing, hysteresis) and configuration toggles; collaborate with ML and Product to calibrate and adapt over time.
- Optimize apps for latency, memory, and power efficiency; select runtime delegates (NNAPI/GPU/DSP) when appropriate; profile and tune cold‑start and steady‑state performance.
- Build developer‑facing tools and lightweight UIs (Jetpack Compose) for debugging, telemetry visualization, tracing, threshold management, and runtime selection.
- Implement privacy‑preserving telemetry and evaluation hooks (e.g., precision/recall estimates, false positive rates) without retaining raw audio or sensitive data.
- Establish quality gates, including unit/instrumentation tests, Compose UI tests, and contract tests that validate model interfaces, shapes, and versioning.
- Document architecture decisions, risks, and integration learnings; contribute to productionization strategies and ongoing platform improvements.
- Collaborate across multidisciplinary teams to ensure smooth deployment, maintainability, and scalability of ML features in‑vehicle.
Skills and Qualifications: Required
- Production‑level Android development in Kotlin, with expertise in coroutines/Flows, dependency injection, background execution, and modern architecture patterns (MVVM/MVI).
- Proven experience integrating ML models on Android (TFLite/MediaPipe/ONNX Runtime) and/or invoking cloud models from mobile apps with secure auth, retries, and timeouts.
- Strong understanding of Android audio capture (AudioRecord), streaming pipelines, and latency‑aware processing.
- Performance engineering skills, including profiling with Perfetto/Traceur/Android Studio Profiler; startup, jank, and memory optimization.
- Experience with testing frameworks (JUnit, instrumented tests, Compose testing) and interface/contract testing for model boundaries.
- Effective collaboration and documentation skills suited to fast‑moving development cycles.
- Strong problem‑solving mindset with attention to detail, clarity in communicating trade‑offs, and the ability to operate autonomously.
- Familiarity with GitHub Pull Request and code review processes.
Preferred
- AAOS or embedded Android experience; knowledge of in‑vehicle UX and system constraints.
- Background in experiment frameworks, analytics, or safety‑relevant alerting.
- Understanding of privacy‑preserving telemetry and compliance requirements.
- Familiarity with the Android Neural Network API (NNAPI).
- Experience with Qualcomm's AI Hub and AI Runtime SDKs.
Seniority level
Employment type
Job function
- Motor Vehicle Manufacturing
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