NimbleEdge

Want to work with us? We're hiring! ?? https://lnkd.in/gEWnCTen We are looking for a Software Engineer (C++) to help us build the low-level infrastructure that makes AI truly intelligent—right where it matters. - Work on cutting-edge AI and ML infrastructure - Take ownership in a fast-moving startup backed by top AI minds - Tackle complex, high-performance computing challenges - Competitive compensation, ESOPs, and flexible work (remote-friendly!) If you love C++, have 5+ years of experience, and are passionate about high-performance systems and AI runtimes (ONNX, PyTorch, etc.), let's talk! At NimbleEdge, we’re building on-device AI that is private, scalable, and cost-efficient, powering some of the largest mobile apps in the US and India. Backed by top VCs and AI leaders from OpenAI, Meta, PayPal, and UC Berkeley, we're pushing the boundaries of personalized AI on mobile. #hiring #AI #machinelearning #softwareengineer #startupjobs

From spaghetti to sleek: How to write maintainable JNI for Android JNI (Java Native Interface) can be a powerful tool—but if not handled carefully, it can quickly turn into a nightmare of memory leaks, crashes, and unreadable code. At NimbleEdge, we build an on-device AI platform that runs ML models and AI workflows directly on resource-constrained devices. To maximize performance and efficiency, our core platform is optimized in C++ and interfaces with Android using JNI, making JNI maintainable critical to our success. In Part 1 of this series https://lnkd.in/gghDNyMC, Naman Anand shares: ?? How major companies like Spotify, WhatsApp, and Chrome leverage JNI for performance gains ?? The common pitfalls that make JNI frustrating (memory leaks, local reference overflows, verbose boilerplate) ?? Four practical fixes to make JNI more maintainable—including RAII, smart pointers, shadow classes, and dynamic registration ?? Real-world examples from NimbleEdge’s own learnings in optimizing JNI for AI applications If you're working with JNI or looking to improve its maintainability, check out the full breakdown. It’s time to make JNI fun again! #JNI #AndroidDev #OnDeviceAI #NimbleEdge

AI shouldn’t just be smart—it should be seamless. GenAI applications mostly operate in the cloud, but scaling them for millions of users? That’s expensive, slow, and risky for privacy. At NimbleEdge, we’re pioneering on-device AI—where real-time data processing and feature computation happen directly on user devices. This means: - Instant AI responses without cloud latency - Privacy-first design with user data staying on-device - Lower costs by reducing cloud dependency In our latest blog, we break down the challenges of on-device AI and how we’re solving them: https://lnkd.in/gsvgxGVu #AI #EdgeComputing #MachineLearning #GenerativeAI #OnDeviceAI

Your cloud costs are too high! And they’re only getting worse... Did you know that on-device AI compute is growing 37% faster than public cloud infrastructure? That means the cost per unit of compute on smartphones is falling faster than cloud prices, making it the smarter, more scalable, and more cost-efficient choice for AI-driven applications. Companies relying solely on cloud infrastructure are leaving millions on the table. And in places like India, smartphone compute capacity is already 9× larger than the country’s total cloud capacity—a staggering opportunity for cost savings. If you're still running all your AI/ML workloads in the cloud, it's time to rethink your strategy. Read our latest blog to learn why on-device AI is the future of compute. ?? https://lnkd.in/g5AqU5Bs

???? ???? ???????????????????? ?????????? ????????–?????? ???? ????, ????????????? AI is certainly the talk of the town, but when it comes to real-world impact, how is it truly affecting our daily lives? As we step into 2025 after two years of rapid AI innovation, let’s ask ourselves: ???????? ???? ???? 2025 ???? ?????? ????????????????, ????????????????????? And, if so, what do we need to do to get there? Check out our latest blog post to learn more ?? https://lnkd.in/gew3kgP2 #AIForEveryoneEverywhere #RealTimeAI #OnDeviceAI #BuildingExperiencesAndNotJustAI #AIForTheBestofUS #AIForTheRestOfUs #EdgeAI Neeraj Poddar Varun Khare

??????????? ?????? ???????????????????? ???? ???????????????? ????-???????????? ?????????? ???????????? ???????????????????? ?????? ???????????????????? ????????-???????? ???????? ?????????????????? ?? In our post last week, we shared how NimbleEdge is enabling AI teams to capture event streams on-device for use in training session-aware personalization model ?? However, event payloads may often be large in size, and require filtering and processing before transfer to cloud storage for use in training. NimbleEdge enables AI teams to execute this event stream processing directly on-device using Python scripts! Click on the link below to learn more: https://lnkd.in/gzCgqzUH

?? ???????? ?????? ???????? ???? ?????????? ?????? ???????????????????? ???? ???????????????? ???????????????????? ?????????? ???????????? ???????? ???????????????????? ?????? ??????????????-?????????? ???? ?? Session-aware personalization, i.e. adapting app experiences to real-time user inputs, is being leveraged by several pioneering apps such as Netflix, Instacart, AirBnB and Alibaba to boost engagement and conversion. ?? However, training models to enable such session-aware personalization requires building massive, accurate event stream datasets, which is time-taking and involves large cloud transfer costs With NimbleEdge's on-device event stream capture capabilities, requisite user clickstream data is captured and stored securely on their own devices, with provisions for seamless and cost-efficient transfer to cloud storage for training. Click on the link below to learn more: https://lnkd.in/gNHvwccZ

Watch this insightful conversation between Dr. Vinesh Sukumar of Qualcomm and Dan Costa as they discuss their expectations for the evolution of on-device AI ?? In this discussion, they cover: ?? Why we need AI capabilities on our phones ?? Edge AI beyond mobile (e.g., in automotive applications) ?? Challenges in on-device AI (e.g., model size, continuous learning) ?? Privacy preservation and personalization with on-device AI ? Future developments (e.g., on-device AI agents) ?? With mobile hardware capabilities improving exponentially, mobile apps can now leverage on-device AI to cost-effectively deliver scalable and privacy-preserving AI experiences that are impossible to achieve with cloud infra ?? At NimbleEdge, we're building an on-device AI platform that enables apps to achieve this effortlessly. NimbleEdge platform provides tools to ingest raw user data, deploy, execute, and monitor on-device models, as well as state-of-the-art AI models optimized for mobile deployment To learn more, visit nimbleedge.com or contact us at [email protected] Watch the conversation here: https://lnkd.in/gpbfFqSh

Check out this recent blog by Google about on-device AI, which provides a clear overview of: ? What on-device AI really means ?? How today’s smartphone hardware powers on-device AI ?? The cost and latency benefits of on-device AI OS providers for smartphones and laptops have shipped on-device LLMs to deliver several valuable features. However, mobile apps still struggle to leverage the benefits of on-device AI due to the complexities of on-device model deployment and execution. At NimbleEdge, we’re solving this challenge with an on-device AI platform that enables edge modeling, deployment, event ingestion, model execution, and monitoring. Learn more at nimbleedge.com or reach out to us at [email protected]. ?? https://lnkd.in/efvEpBbJ

?????????? ?????? ???????????????????? ?????????????????????? ???????? ????????????, Arpit Saxena'?? ???????????? ???????? ???? ???????????????? ???????????? ???????????? ??? In the blog, Arpit breaks down hardware memory models and the complexities of relaxed concurrency, focusing mostly on ARM and IBM POWER architectures, while also motivating the C++ memory model ?? Ideal for developers looking to deepen their understanding of low-level memory synchronization, this blog offers valuable insights into ensuring correctness while squeezing out performance! https://lnkd.in/gS9_TqX7