| Job Position | Company | Posted | Location | Salary | Tags |
|---|---|---|---|---|---|
OpenGradient | United States | $126k - $127k | |||
Entangle Labs | Dubai, United Arab Emirates | $84k - $106k | |||
Brave | London, United Kingdom | $84k - $108k | |||
Whatnot | San Francisco, CA, United States | $205k - $275k | |||
| Learn job-ready web3 skills on your schedule with 1-on-1 support & get a job, or your money back. | | by Metana Bootcamp Info | |||
CEF AI | San Francisco, CA, United States | $150k - $180k | |||
Binance | Abu Dhabi, United Arab Emirates |
| |||
Launchpadtechnologiesinc | Remote | $126k - $127k | |||
MoonPay | Barcelona, Spain | $126k - $127k | |||
Bluesky | Remote | $115k - $180k | |||
Bluesky | Remote | $123k - $180k | |||
Truflation | Remote | $90k - $180k | |||
Truflation | Remote | $105k - $180k | |||
Truflation | Remote | $43k - $54k | |||
Heretic/Arcade | San Francisco, CA, United States | $21k - $70k | |||
Genies | San Mateo, Portugal | $165k - $230k |
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.Â
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
Is machine learning a good career?
Yes, machine learning is a rapidly growing field and can be a very promising career option for those interested in it
As businesses and industries increasingly rely on data to drive decision-making, there is a growing need for skilled professionals who can analyze and make sense of this data
Machine learning, which involves developing algorithms that can learn from and make predictions on large datasets, is a crucial part of this process
Machine learning careers can range from data analysts, machine learning engineers, data scientists, and more
These professionals work in a variety of industries, including finance, healthcare, e-commerce, and technology
The demand for machine learning experts is high, and the salaries in this field are also generally quite competitive
However, it's important to note that machine learning can be a complex field that requires a strong background in mathematics, statistics, and computer science
It also requires ongoing learning and staying up-to-date with the latest developments and tools in the field
If you enjoy working with data, have a strong interest in programming, and are willing to put in the effort to stay current with developments, a career in machine learning can be very rewarding.