| Job Position | Company | Posted | Location | Salary | Tags |
|---|---|---|---|---|---|
Heretic | San Francisco, CA, United States | $97k - $150k | |||
FLock.io | Remote |
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Numus | Remote | $63k - $70k | |||
CERE NETWORK | Remote |
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| Learn job-ready web3 skills on your schedule with 1-on-1 support & get a job, or your money back. | | by Metana Bootcamp Info | |||
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Cere Network | Europe |
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Okcoin | San Jose, CA, United States | $76k - $150k | |||
OP3N | Los Angeles, CA, United States | $21k - $64k | |||
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Kraken Digital Asset Exchange | Remote | $63k - $120k | |||
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Magic Eden | San Francisco, CA, United States | $180k - $220k | |||
Hologram Labs | New York, NY, United States | $122k - $156k | |||
Kraken Digital Asset Exchange | Remote | $63k - $75k | |||
Heretic | San Francisco, CA, United States | $87k - $120k |
ML Ops Engineer (Stealth Heretic PortCo)
Responsibilities
- Collaborate with cross-functional teams to deploy and maintain AI models in production environments, ensuring scalability, reliability, efficiency, and robustness
- Orchestrate model serving to accommodate our unique infrastructure in a scalable manner
- Maintain backend planning and optimize GPU capacity continuously
- Build tools for end-to-end ML model deployment and lifecycle management
- Build tools to monitor model performance
- Recommend options for end-to-end Ops pipelines that are needed to drive various business plans
- Stay up-to-date with the latest advancements in AI technologies and research, and apply them to enhance performance and capabilities
Qualifications
- Bachelor's or Master's degree in Computer Science, AI/ML, or a related field
- 2+ years of professional ML deployment experience on scale, preferably MLOps for LLMs and/or diffusion models
- 2+ years of experience with cloud platforms (e.g., AWS, Azure, OCI, Google Cloud) and experience with deploying AI models in cloud-based environments
- Proficiency in containerization technologies as Docker, inference servers as Triton and container orchestration platforms as Kubernetes
- Proven experience in working with and scaling GPUs
- Experience partnering with back-end & front-end eng to tie AI/ML infrastructure to a scaled front-end experience
- Strong knowledge of Python, with experience in popular machine learning libraries (e.g., TensorFlow, PyTorch, Spark)
- Solid understanding of machine learning concepts and algorithms.
- Extensive Linux troubleshooting experience
- Excellent problem-solving and analytical thinking skills, with a strong attention to detail
- Effective communication and teamwork abilities, with the capacity to work in a fast-paced, collaborative environment
Nice to Haves
- Experience working with Stable Diffusion models
- Contributions to open-source AI projects or publications in relevant conferences or journals
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.