Collaborating with CTO/PM/BD to design FedML Blockchain Layer 1 and Layer 2 Infrastructure to support on-chain AI and AI marketplace
Research and development on tokenomics for the data and AI community
Lead a blockchain engineering team
Working with the open-source community by collaborating with external contributors on our codebase
Advanced proficiency in programming languages, such as Go, Rust, C++, Java, and Python.
Extensive experience in common algorithms, data structures, and their computation, communication, and memory complexities; prior experiences in performance optimization preferred
Experience in blockchain infrastructure development, proficiency with Smart Contract (e.g., Solidity), the Ethereum Virtual Machine (EVM), consensus algorithm, wallet interfaces, and RPCs, with production-level deployments of non-trivial protocols and related security audits
Experience in cryptography, especially in zero-knowledge proof
Strong ability to develop and debug in distributed systems, P2P networks, etc.
A good understanding of machine learning and AI is preferred but not required
Passion for working with startups and fast-paced, ambitious environments is a plus
Good communication and writing skills in an English environment
Good knowledge of how cryptocurrencies & trading systems work
Exposure to smart contracts, decentralized governance, master nodes, and blockchains
Understanding of financial instruments (arbitrage, providing market-making features)
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.
When applying, mention the word CANDYSHOP to show you read the job post completely. This is a beta feature to avoid spam applicants. Companies can search these words to find applicants that read this and see they are human RMy4yMzMuMjIxLjkwM