Machine Learning Engineer

What we are building

OpenGradient is building native on-chain AI inference, designed to deliver seamless and scalable inference secured by state-of-the-art cryptography. The OpenGradient Network is an EVM blockchain network that is a composable execution layer for on-chain AI. The network features access to scalable and secure model inference, allowing developers to seamlessly leverage AI models in composable smart contracts to create powerful decentralized applications and enable new use-cases.

Who we are

Our team is made up of experienced engineers from companies like Two Sigma, Palantir and Google, dedicated to driving the next generation of decentralized AI use-cases on the blockchain. 

Join us on our mission to decentralize AI!

The Role

We are seeking a self-driven and motivated Machine Learning Engineer with a specialized focus on building robust infrastructure tailored for handling large-scale inference workloads for AI and ML models. You will spearhead the design, development, and optimization of high-performance AI/ML systems capable of supporting real-time and batch processing requirements across diverse domains. You will collaborate closely with cross-functional teams to architect and implement cutting-edge inference pipelines and infrastructure, ensuring the reliability, efficiency, and scalability of our machine learning model deployments. Your responsibilities will include:

• Design and implement scalable and efficient machine learning inference infrastructure and architecture to support real-time and batch processing requirements.

• Develop deployment pipelines and tools for deploying machine learning models into production environments, including containerization (e.g., Docker) and orchestration (e.g., Kubernetes).

• Optimize model inference performance and resource utilization through techniques such as model quantization, pruning, and acceleration (e.g., GPU/TPU utilization, model caching).

• Continuously evaluate and improve the performance of machine learning models and infrastructure through experimentation and optimization techniques.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field

• Strong background in machine learning, deep learning, and statistical modeling with hands-on experience in developing and deploying ML infrastructure

• Proficiency in programming languages such as Python, Java, C++, Go

• Extensive experience with popular ML frameworks (e.g., TensorFlow, PyTorch, HuggingFace) and technologies (e.g. CUDA, ONNX)

• Experience as a software engineer with deep understanding of algorithms and data structures

• Have familiarity with the latest AI and ML research and working knowledge of how these systems are efficiently implemented.

Nice to have

• Familiarity with MLOps, DataOps

• Experience at fast-growing startups or companies

• Interest in blockchain technology and its benefits such as privacy, computational integrity and censorship-resistance

• Experience supporting ML or AI Infrastructure, such as Triton