London, EnglandGlobalgreenhouse2026-10-30
Warum echter KI-Job: The role is explicitly focused on defining, researching, and deploying machine learning features for a core AI product (Fin - AI Customer Agent). The job description heavily emphasizes ML algorithms, model evaluation, and productionization.
Fin , now part of Salesforce, is on a mission to help businesses provide perfect customer experiences. Our AI Agent Fin is the highest-performing AI Customer Agent on the market today, enabling businesses to deliver impeccable, always-on customer support across the customer journey, from service, t…
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Dublin, IrelandGlobalgreenhouse2026-10-30
Warum echter KI-Job: The role is explicitly focused on building and deploying machine learning models for a core AI product (Fin - AI Customer Agent). The job description details tasks such as identifying ML opportunities, researching algorithms, conducting data analysis, and bri…
Fin , now part of Salesforce, is on a mission to help businesses provide perfect customer experiences. Our AI Agent Fin is the highest-performing AI Customer Agent on the market today, enabling businesses to deliver impeccable, always-on customer support across the customer journey, from service, t…
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Bangalore, IndiaUSAgreenhouse2026-10-30
Warum echter KI-Job: The role explicitly focuses on building and improving core AI components like search engines, language models, and personalization algorithms. The tasks listed are heavily centered around ML model development, training, and deployment.
About Glean: Glean is the Work AI platform that helps everyone work smarter with AI. What began as the industry’s most advanced enterprise search has evolved into a full-scale Work AI ecosystem, powering intelligent Search, an AI Assistant, and scalable AI agents on one secure, open platform. With…
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San Francisco, CAUSAgreenhouse2026-10-30
Warum echter KI-Job: The role explicitly focuses on research and development of post-training techniques for LLMs, including SFT, RLHF, and reward modeling. The job description highlights the application of these techniques to enhance LLM capabilities and solve core AI problems.
Scale works with the industry’s leading AI labs to provide high quality data and accelerate progress in GenAI research. We are looking for Research Scientists and Research Engineers with expertise in LLM post-training (SFT, RLHF, reward modeling). This role will focus on optimizing data curation an…
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San Francisco, CAUSAgreenhouse2026-10-30
Warum echter KI-Job: The role explicitly focuses on research and development of LLMs, evaluation methodologies, and addressing failure modes in generative AI models. The core responsibilities are deeply rooted in AI/ML techniques.
Scale works with the industry's leading AI labs to provide high quality data and accelerate progress in GenAI research. We are looking for Research Scientists and Research Engineers with expertise in LLM post-training (SFT, RLHF, reward modeling) and evaluation. This role is on the evaluation pod w…
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San Francisco, CAUSAgreenhouse2026-10-30
Warum echter KI-Job: The role is explicitly focused on evaluating and improving the quality, alignment, and safety of production LLMs. Responsibilities directly involve ML model evaluation, data analysis, and building evaluation methodologies. The entire job revolves around AI/ML.
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working to…
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Anthropic · Remote-Friendly US (Travel Required)
95/100
Remote-Friendly US (Travel Required)USAgreenhouse2026-10-30
Warum echter KI-Job: The role is explicitly focused on building and scaling a data-driven culture to ensure safe and beneficial AI. The responsibilities center around analyzing user behavior related to safety, defining key metrics for success, and building systems to improve prod…
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working to…
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Paris, FranceUSAFullTimeashby2026-10-30
Warum echter KI-Job: The role explicitly focuses on designing, building, and deploying AI systems for enterprise customers. The description emphasizes hands-on engineering with AI models, agents, and data, and requires deep technical expertise in AI/ML concepts. The core responsi…
About the Team OpenAI’s Applied AI Engineering team helps organizations turn frontier AI capabilities into safe, reliable, and high-impact production systems. We work with customer executives, product and engineering teams, security leaders, and transformation teams to identify valuable opportuniti…
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San FranciscoUSAFullTimeashby2026-10-30
Warum echter KI-Job: The role is explicitly focused on building, evaluating, and improving mitigations for harms from AI systems. It requires deep technical skills in data science, machine learning, and model evaluation, with a strong emphasis on identifying and addressing risks…
About the Team The Preparedness team is an important part of the Safety Systems https://openai.com/safety/safety-systems org at OpenAI, and is guided by OpenAI’s Preparedness Framework https://openai.com/index/updating-our-preparedness-framework/. Frontier AI models have the potential to benefit al…
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San FranciscoUSAFullTimeashby2026-10-30
Warum echter KI-Job: The role is explicitly focused on building and improving AI agents (Codex), working directly with LLMs, and turning research into production systems. The tasks described are overwhelmingly centered around AI/ML concepts.
About the Team The Codex Core Agent team builds the kernel of Codex. We own making the agent better, accelerating research, and making those improvements real in production for our users. That means working across the systems that make Codex actually function as an agent in the real world: the prod…
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