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AI Scientist United States, San Francisco Jobs

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Principal Data Scientist, AI
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Lead AI and data strategy as a Principal Data Scientist at Atlassian. Leverage 8+ years of expertise in SQL, Python/R, and advanced statistics to solve key business challenges. Enjoy a flexible, distributed-first work culture with comprehensive benefits like health coverage and paid volunteer day...
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United States , Seattle; San Francisco
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167100.00 - 268400.00 USD / Year
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Atlassian
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Until further notice
AI Applied Scientist
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Join Figma's mission to make design accessible as an AI Applied Scientist. Drive fundamental and applied research in Generative AI, building models with SFT, RL, and fine-tuning. Leverage your expertise in PyTorch, LLMs, and software engineering to create magical product features. This role offer...
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United States , San Francisco; New York
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149000.00 - 350000.00 USD / Year
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Figma
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Until further notice
Explore the frontier of intelligent systems with a career as an AI Scientist. This page is your dedicated portal to discover and apply for cutting-edge AI Scientist jobs, connecting you with opportunities to shape the future of technology. AI Scientists are the master architects behind artificial intelligence, specializing in the research, development, and deployment of advanced machine learning and deep learning models. Their work transcends basic data analysis, focusing on creating novel algorithms and systems that can learn, reason, and make autonomous decisions. This profession sits at the intersection of computer science, statistics, and domain-specific expertise, demanding both profound theoretical knowledge and robust practical engineering skills. Professionals in this role typically engage in the full AI lifecycle. They begin by researching and prototyping innovative algorithms to solve complex problems, such as natural language processing, computer vision, or reinforcement learning. A core responsibility is the meticulous cleaning, processing, and analysis of vast datasets to train these models effectively. Beyond experimentation, AI Scientists are deeply involved in the engineering required to transition models from research notebooks to scalable, reliable production systems. This includes building robust data pipelines, optimizing model performance for inference speed and accuracy, and implementing monitoring frameworks to ensure models remain effective over time. Collaboration is key; they frequently work alongside software engineers, product managers, and domain experts to integrate AI capabilities into real-world applications and products. The typical skill set for AI Scientist jobs is both deep and broad. A strong educational foundation, usually a Master’s or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field, is common. Expertise in programming languages like Python and R is essential, coupled with mastery of libraries and frameworks such as TensorFlow, PyTorch, scikit-learn, and Keras. Proficiency in data manipulation using SQL and big data technologies is also standard. Crucially, successful AI Scientists possess strong mathematical skills in linear algebra, calculus, probability, and statistics. They are expected to write clean, production-level code and adhere to software engineering best practices, including version control (Git), continuous integration/deployment (CI/CD), and comprehensive testing. Soft skills like problem-solving, critical thinking, and the ability to communicate complex technical concepts to non-technical stakeholders are equally vital. Whether contributing to strategic capability development, optimizing customer-facing systems, or pushing the boundaries of algorithmic research, AI Scientists are in high demand across industries like technology, finance, healthcare, and defense. If you are driven by innovation and possess the blend of research acumen and engineering rigor required to build the next generation of AI, browsing the AI Scientist jobs listed here is your first step toward a transformative career. Find your role in advancing intelligent automation and machine cognition today.

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