Responsibilities

• Participate in the development of MLOps/AIOps machine learning platform and open source communities
• Responsible for the foundational research and product development, and continuously improve the R&D efficiency
• Responsible for feature development, algorithm optimization of the platform, improving user experience and usability through cutting-edge or mature technologies
• Participate in or lead design reviews with peers and stakeholders to decide amongst available technologies
• Review code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency)
• Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback

Minimum Qualifications

• Bachelor’s degree or equivalent practical experience in computer science or related areas.
• 2 years of experience with software development in one or more programming languages (Python, Java, JavaScript, C/C ), or 1 year of experience with an advanced degree
• 2 years of experience with data structures or algorithms in either an academic or industry setting
• Good communication and writing skills in English environment

Preferred Qualifications

• Proficient in Python language, familiar with typical deep learning frameworks (TensorFlow/PyTorch) and models such as CNN, Transformer, GBDT, LR, etc.
• Experience in developing MLOps features, including workflow orchestration, model training, model serving, monitoring/observability, versioning of data, code, model, data pipeline, logging, etc.
• Familiar with communication backends (MPI, NCCL, RPC, MQTT), GPU CUDA, and other core modules of deep learning frameworks, those who have participated in the development of specific modules of famous deep learning frameworks are preferred
• Experience with federated learning, distributed training on large-scale model is preferred
• Combine the platform and the open source library to improve the training efficiency of deep learning end-to-end through task scheduling, elastic disaster recovery, performance optimization and other measures, involving K8S/KubeFlow, network optimization, and distributed training

About the Job

FedML, Inc. (https:/fedml.ai) empowers our clients to build & scale any machine learning or artificial intelligence models anywhere. That includes the latest foundation models as well as more traditional ML models. Our products cover both training, serving with a low-code UI MLOPs & LLMOps platform. We also offer a Federated Machine Learning solution for cross-silo training for data privacy sensitive applications.

Our earliest products power federated machine learning missions for clients in several industries, where data privacy, low latency serving, and low cost of data storage are important to the client. Our easy-to-use FedML MLOps solution enables data science and machine learning engineering to work seamlessly together to deploy & manage their model to production machines. Our federated learning and serving solutions support siloed edge devices, smartphones, and IoT.

Our next generation of solutions includes geo-distributed machine learning and serving that continues our tradition of delivering easy-to-use, simple, low-cost, and enterprise grade MLOPs solutions. Our MLOps and evolving LLMOps platform will always empower experimentation, observability, evaluation, governance, and collaboration for our clients’ AI & ML training and serving needs, as well as other general computing needs.

FedML supports vertical solutions across a broad range of industries (healthcare, finance, insurance, automotive, advertising, smart cities, IoT etc,) and applications (computer vision, natural language processing, data mining, and time-series forecasting). Its core technology is backed by more than 3 years of cutting-edge research of its co-founders who are recognized leaders in the federated machine learning community.

FedML's researchers and software engineers and product teams are busy developing the next-generation FedML platform for machine learning and artificial intelligence and we're looking to grow our team with skilled professionals who bring fresh ideas from all areas, including machine learning and its applications, computer vision, natural language processing, large-scale system design, distributed/cloud computing/systems, MLOps, security/privacy, mobile/IoT systems, and networking. We’re an early stage startup, hence you will work on projects which are critical to our customers' and our business needs. If you love to learn, and love to convert ideas into real and scalable machine learning infrastructure products and applications, FedML may be a great place for you.

Location

Our HQ is in Sunnyvale California. Preference is for someone local who can be at our office regularly. Hybrid is ok.

How to apply

If you are interested, please apply via the link.