Machine Learning Engineer
As a Data Scientist (Algorithm Engineer) in Delivery, you will work closely with Simulation Engineers, Machine Learning Engineers, and customers to understand and define engineering and physics challenges, while providing technical leadership to your team. Your responsibilities include leading the pre-processing and analysis of complex data to prepare it for predictive modelling, establishing best practices and methodologies for your team, architecting and developing innovative deep learning models combined with optimisation methods to predict and control physical systems, and taking full responsibility for the quality, accuracy, and impact of your work and your team's work. You will design, build, and test data pipelines that are reliable, scalable, and easily deployable in production environments, lead cross-functional collaboration to ensure model integration with simulations, drive internal research and product development, mentor junior team members, lead communication and presentations with technical teams and customers, and represent the company as a technical authority when visiting customer sites globally. Additionally, as a senior member, you will influence technical direction and shape future solutions and products while developing leadership skills.
Machine Learning Engineer, API Multicloud
The role involves partnering with strategic customers and internal teams to define target model behaviors, diagnose failure modes, and translate real-world needs into training, evaluation, and system requirements. The engineer will build and scale production machine learning systems for model customization, post-training, and fine-tuning-as-a-service workflows. Responsibilities include investigating whether training and customization workflows produce the intended outcomes and identifying necessary changes to data, evaluation, training, or infrastructure to improve performance. The engineer will collaborate with backend and infrastructure engineers to integrate ML capabilities into AWS-native API environments and feed learnings from partner deployments back into the platform by proposing and implementing improvements to post-training systems, tooling, APIs, and developer workflows. The role requires close work with Research and Applied teams to bring model improvements, training workflows, and evaluation best practices into production. Designing systems that allow strategic partners and enterprise customers to safely customize OpenAI models for high-value use cases is also a key responsibility. Additionally, the role involves debugging and improving complex systems spanning model behavior, training data, APIs, distributed infrastructure, and customer-facing product surfaces. The engineer must operate with high ownership in a 0 to 1 environment where requirements are ambiguous, systems are evolving quickly, and reliability matters.
ML Engineer, Post-Training and Evaluation
As a ML Engineer on Reflection's Applied AI team, you will fine-tune Reflection's open-weight models for specific customer use cases by preparing datasets, configuring training runs including SFT, preference optimization, and reinforcement fine-tuning, and iterating based on evaluations. You will build and maintain evaluation infrastructure by designing eval suites, curating test sets, establishing baselines, and measuring model improvements. You will prepare training data from raw customer inputs by inspecting data quality, cleaning and formatting datasets, identifying adversarial or noisy samples, and building reproducible data pipelines. You will debug and diagnose training and inference issues by interpreting loss curves, catching data quality problems, and identifying training dynamics issues. Additionally, you will support end-to-end deployments of fine-tuned models across hybrid environments such as public cloud, VPC, and on-premises, ensuring inference performance and reliability in production. You will also contribute to evolving playbooks, evaluation benchmarks, and best practices within the fine-tuning and evaluations practice.
Member of Engineering (Post-training)
Research and experiment on ways to specialize foundational models to agentic use cases, build and maintain data and training pipelines, keep up with latest research and be familiar with state of the art in LLMs, alignment, synthetic data generation, and code generation, design, analyze, and iterate on training, fine-tuning, and data generation experiments, write high-quality and pragmatic code, and work as part of a team by planning future steps, discussing, and communicating clearly with peers.
Member of Technical Staff - ML Performance
The role involves engineering work focused on making machine learning systems performant at scale. This includes contributing to open-source projects and enhancing Modal's container runtime to improve the throughput and reduce the latency of language and diffusion models.
AI/ML Engineer, Rome
Develop, train, and optimize machine learning models for various mobile app features. Research and implement state-of-the-art AI techniques to improve user engagement and app performance. Collaborate with cross-functional teams to integrate AI-driven solutions into applications. Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring. Analyze large datasets to derive insights and drive data-driven decision-making. Stay updated with the latest AI trends and best practices, incorporating them into development processes. Optimize AI models for mobile environments to ensure high performance and low latency.
AI/ML Engineer, Paris
Develop, train, and optimize machine learning models for various mobile app features. Research and implement state-of-the-art AI techniques to improve user engagement and app performance. Collaborate with cross-functional teams to integrate AI-driven solutions into applications. Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring. Analyze large datasets to derive insights and drive data-driven decision-making. Stay updated with the latest AI trends and best practices, incorporating them into development processes. Optimize AI models for mobile environments to ensure high performance and low latency.
AI/ML Engineer, Madrid
Develop, train, and optimize machine learning models for various mobile app features. Research and implement state-of-the-art AI techniques to improve user engagement and app performance. Collaborate with cross-functional teams to integrate AI-driven solutions into applications. Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring. Analyze large datasets to derive insights and drive data-driven decision-making. Stay updated with the latest AI trends and best practices, incorporating them into development processes. Optimize AI models for mobile environments to ensure high performance and low latency.
AI/ML Engineer, London
Develop, train, and optimize machine learning models for various mobile app features. Research and implement state-of-the-art AI techniques to improve user engagement and app performance. Collaborate with cross-functional teams to integrate AI-driven solutions into applications. Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring. Analyze large datasets to derive insights and drive data-driven decision-making. Stay updated with the latest AI trends and incorporate them into development processes. Optimize AI models for mobile environments to ensure high performance and low latency.
AI/ML Engineer, Berlin
Develop, train, and optimize machine learning models for various mobile app features. Research and implement state-of-the-art AI techniques to improve user engagement and app performance. Collaborate with cross-functional teams to integrate AI-driven solutions into the applications. Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring. Analyze large datasets to derive insights and drive data-driven decision-making. Stay updated with the latest AI trends and best practices, incorporating them into development processes. Optimize AI models for mobile environments to ensure high performance and low latency.
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