Machine Learning Jobs in Web3
258 jobs found
| Job Position | Company | Posted | Location | Salary | Tags |
|---|---|---|---|---|---|
Heretic/Arcade | San Francisco, CA, United States | $21k - $70k | |||
Genies | San Mateo, Portugal | $165k - $230k | |||
Brave | London, United Kingdom | $18k - $80k | |||
Worldcoinorg | New York, NY, United States | $105k - $150k | |||
| Learn job-ready web3 skills on your schedule with 1-on-1 support & get a job, or your money back. | | by Metana Bootcamp Info | |||
Menyala | Singapore, Singapore | $129k - $132k | |||
Menyala | Singapore, Singapore | $129k - $132k | |||
Menyala | Singapore, Singapore | $129k - $132k | |||
Coinbase | Remote | $175k - $237k | |||
Kraken | United States | $63k - $87k | |||
Sahara | San Francisco, CA, United States | $32k - $54k | |||
Kraken | United States | $94k - $101k | |||
Pundi X | Singapore, Singapore | $112k - $150k | |||
Whatnot | New York, NY, United States | $175k - $265k | |||
Heretic | San Francisco, CA, United States | $21k - $70k | |||
Wyndlabs | Remote | $150k - $220k |
Spring Semester - Applied AI/ML Engineering Intern
Responsibilities
- Collaborate with cross-functional teams to train and fine-tune machine learning models and systems for consumer-focused ventures.
- Build machine learning models for various generative AI applications, including text-to-image diffusion models, large language models, and other emerging generative AI.
- Develop multi-model architectures to meet product & business requirements for new venture concepts.
- Collect, preprocess, and analyze data to extract meaningful insights and improve the performance of AI models.
- Deploy and maintain AI models in production environments, ensuring scalability, reliability, and efficiency.
- Stay up-to-date with the latest advancements in AI technologies, contribute to research environment, and apply them to enhance the performance and capabilities of our ventures.
- Communicate complex AI concepts and solutions effectively to both technical and non-technical stakeholders.
Qualifications
- Currently enrolled in a Bachelor's, Master's, or PhD degree in Computer Science, Mathematics, Artificial Intelligence / Machine Learning, or a related field.
- Prior professional experience working on applied AI projects, preferably in a product development or research environment.
- Strong knowledge of Python and familiarity with Linux with experience in popular machine learning libraries (e.g., TensorFlow, PyTorch).
- Solid understanding of machine learning concepts and algorithms.
- Experience with training and fine-tuning AI models and working with large-scale datasets.
- Proficiency in data preprocessing, feature engineering, and exploratory data analysis.
- Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) and experience with deploying AI models in cloud-based environments.
- 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 fine-tuning Stable Diffusion models for specific product use cases.
- Experience fine-tuning OpenAI GPT models for specific product use cases.
- 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.