As a member of the ML team you will research, design, implement, optimize and deploy deep learning to the cloud and edge devices.
A typical day to day includes reading deep learning code/papers, implementing described models and algorithms, adapting them to our setting, driving up internal metrics, working with downstream engineers to integrate neural networks to run efficiently on edge devices, and incrementally tracking and improving feature performance based on client requirements. A strong candidate will ideally possess at least one strong expertise in the following areas, and at least a familiarity in others.
- Train machine and deep learning models on the cloud to perform visual recognition tasks, such as segmentation and detection from scratch or using transfer-learning.
- Develop state-of-the-art algorithms in one or all of the following areas: deep learning (convolutional neural networks), object detection/classification, tracking, multi-task learning and face recognition.
- Optimize deep neural networks and the associated preprocessing/postprocessing code to run efficiently on an embedded devices and low power GPUs.
- Customize open-source, state-of-the-art algorithms deep learning models to fit client’s needs.
- Generating desired outputs and sending the payload through Kafka, MQTT etc ..
- The team operates in a production setting. An ideal candidate has strong software engineering practices and is very comfortable with Python and C++ programming, debugging/profiling, and version control.
- We train neural networks on a local gpu or cloud settings. An ideal candidate is comfortable in cluster environments and understands the related computer systems concepts (CPU/GPU interactions/transfers, latency/throughput bottlenecks during training of neural networks, CUDA, pipelining/multiprocessing, etc).
- We are at the cutting edge of deep learning applications. The ideal candidate has a strong understanding of the under the hood fundamentals of deep learning (layer details, backpropagation, etc). Additional requirements include the ability to read and implement related academic literature and experience in applying state of the art deep learning models to computer vision (e.g. segmentation, detection) or a closely related area (speech, NLP).
- Experience with PyTorch, or at least another major deep learning framework such as TensorFlow, MXNet.
- Some experience with data science tools including Python scripting, numpy, scipy, matplotlib, scikit-learn, jupyter notebooks, bash scripting, Linux environment.
- Experince with big data tools such as Kafka, Spark, hadoop is huge plus.
What We Offer:
- An opportunity to change the world and work with some of the best and smartest people in the field.Growth potential. We rapidly advance team members who have an outsized impact.Flexible vacation policy.Flexible Remotely/Office PolicySnacks !
- An opportunity to change the world and work with some of the best and smartest people in the field.
- Growth potential. We rapidly advance team members who have an outsized impact.
- Flexible vacation policy.
- Flexible Remotely/Office Policy
- Snacks !