Airalo 招聘 Senior DevSecOps Engineer(西班牙)
About Airalo Alo! Airalo 是全球首个 eSIM 商店,帮助人们在 200 多个国家和地区连接。我们正在构建下一个数字服务,革新电信行业。
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A1 is building a proactive AI chat app for everyday users to bring intelligence to conversations, errands, organising and workflows. Unlike traditional chat-based applications, our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior. As a Member of Technical Staff, Machine Learning, you will build core ML components. You will work on real production systems from day one, learning how large-scale ML behaves outside of research settings. This role is for engineers who want to develop strong systems judgment by shipping, debugging, and iterating on real-world ML. Focus: Build and improve ML components across data, training, evaluation, and inference. Fine-tune and adapt models as part of larger production systems. Implement evaluation and testing to understand model behavior. Help build and maintain data pipelines for real-world and synthetic data. Debug model issues, performance problems, and production incidents. Ship improvements iteratively and learn from real user feedback. Work closely with senior ML engineers and product teams. Work under real production constraints: latency, cost, reliability, and safety. Tech Stack: Python, PyTorch / JAX, Production ML systems running on GPUs. Ideal Experience: Strong foundations in machine learning and modern neural architectures. Some hands-on experience training, fine-tuning, or deploying ML models. Comfortable writing production-quality code and learning new tools quickly. Curious, coachable, and eager to learn from real systems in production. Able to work through ambiguity with guidance and grow ownership over time. Bias toward shipping, iteration, and continuous improvement. Outcomes: ML models in production meet expected accuracy, latency, and reliability targets. Production issues are identified quickly, debugged effectively, and root causes addressed. Data pipelines, training loops, and inference systems are robust, reproducible, and maintainable. Collaborates effectively with engineers, product, and research teams to deliver reliable ML-powered features. Iterations on models and systems are driven by real-world signals and measurable improvements. How We Work: The best products today in the world were built by small, world class teams. We are a high talent density and hands-on team. We make decisions collectively, move at rapid speed, striking a balance between shipping high quality work and learning. Joining our team requires the ability to bring structure, exercise judgment, and execute independently. Our goal is to put in hands of our users a truly magical product. Interview process: If there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews. Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite. We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.
Strong foundations in machine learning and modern neural architectures. Some hands-on experience training, fine-tuning, or deploying ML models. Comfortable writing production-quality code and learning new tools quickly. Curious, coachable, and eager to learn from real systems in production. Able to work through ambiguity with guidance and grow ownership over time. Bias toward shipping, iteration, and continuous improvement.
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About Airalo Alo! Airalo 是全球首个 eSIM 商店,帮助人们在 200 多个国家和地区连接。我们正在构建下一个数字服务,革新电信行业。
Java Software Engineer, AI Imaging (Backend) 位置:远程(拉丁美洲) 工资:每小时31美元
关于该职位 A1正在构建一个主动型AI聊天应用,为日常用户带来对话、事务、组织和工作流程的智能化。与传统基于聊天的应用不同,我们的产品聚焦于实现长流程工作的高可靠性、持久上下文和现实任务完成。系统必须处理多步骤推理,与外部工具交互,并在非确定性模型行为下保持可靠。
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