updated 14 days ago
AI Scientist 2 (AI/ML)
Vacancy from Hirify Global, list of international tech companiesPlus is required to make matches and apply
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Job description
Text:
TL;DR
AI Scientist 2 (AI/ML): Developing and deploying machine learning and AI models for an omnichannel marketing platform with an accent on personalization, relevance algorithms, and generative AI. Focus on training models on large datasets, running A/B tests and statistical analyses, and integrating advanced AI systems into core products.
Location: Atlanta, Georgia, United States
Company
is a financial technology platform providing products including TurboTax, Credit Karma, QuickBooks, and Mailchimp.
What you will do
- Explore, develop, and deploy machine learning models across the end-to-end ML lifecycle.
- Apply supervised, unsupervised, and reinforcement learning, NLP, and generative AI to improve relevance and personalization algorithms.
- Collaborate with product managers, software engineers, designers, machine learning engineers, and analysts on experiments and minimum viable products.
- Discover, process, and train models on large datasets.
- Run A/B tests, perform statistical analysis, evaluate model impact, and communicate results.
- Research generative AI and advanced machine learning trends to inform AI product strategy.
Requirements
- MS or PhD in computer science, statistics, applied mathematics, operations research, or a related field, or equivalent experience.
- At least 1 year of experience with modern AI/ML tools and libraries.
- Proficiency in Python and familiarity with machine learning techniques including classification, regression, neural networks, large language models, recommender systems, NLP, clustering, anomaly detection, or computer vision.
- Familiarity with generative AI, including agentic applications.
- SQL proficiency and comfort working in a Linux environment.
- Ability to explain complex technical topics to technical and non-technical audiences.
Nice to have
- Experience with distributed computing frameworks such as Spark or Ray.
- Understanding of MLOps practices, including version control and CI/CD for ML models.
Culture & Benefits
- Collaborative environment working with scientists and engineers.
- Compensation may include a cash bonus, equity rewards, and benefits.
- Compensation is based on job-related knowledge, skills, experience, and work location.
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