Learning and Development Manager

Product Owner – Extend (H/F) About Horizon Trading Solutions Horizon Trading Solutions is an independent global technology company focused on electronic trading, supporting agency and principal business goals across equities and derivatives. For over 20 years, we have equipped leading capital market participants with algorithmic trading technology and direct connectivity to more than 80 exchanges worldwide. We are currently looking for a Product Owner – Extend to join our Product Management team and define, structure and drive the development of Extend offer ( internal offer) capabilities within our existing multi-asset trading platform. This newly created role combines product strategy and execution: the first objective is to structure and define the Extend scope, build the roadmap and position the offer through market and competitive analysis, before leading its implementation and acting as the internal reference point on Extend topics. Your day to day ? Inside an Agile team you will be dedicated to, using Scrum and DevOps in an international environment ( teams located in France, Hong Kong, Montreal), you will contribute to each step of the development of our solutions (from specification to implementation on client side). You will have the missions to : 1/ Define the scope Propose and build a strategic roadmap on your product scope Discuss with stake holders (CDM, Sales, Product Manager, Client) to understand the context of client requests / change requests / roadmap initiatives Clearly express Product Backlog items – writing or reviewing existing items entered by Client Support, Client Delivery Manager, Engineering team ; except for detailed technical specifications written by Epic Leaders Participate in the pricing of the projects / features Evaluate the relevance and priorities of incoming requests 2/ Manage the backlog Write, detail and validate items in Product backlog Order the items in the Product Backlog to best achieve goa

As an Applied Science Intern, you will join a cross-functional team of engineers and applied scientists building AI-powered features within the Datadog platform. You will contribute to both existing and new products by improving machine learning models, developing new AI capabilities, and helping bring research into production. Working closely with engineering and product partners, you'll gain hands-on experience deploying AI systems at scale while strengthening your product mindset, collaboration, and communication skills. You'll also have opportunities to contribute to cutting-edge research that directly powers customer-facing features. At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You’ll Do: Design solutions for AI use cases by researching and benchmarking state-of-the-art models to identify the best approach Build highly scalable product features using generative AI, machine learning algorithms, and statistical techniques Contribute to the development, deployment, monitoring, and maintenance of production AI models, services, and infrastructure Explore, analyze, and derive insights from the large-scale observability data flowing through Datadog systems Participate in technical knowledge sharing by presenting recent academic research and investigating emerging model architectures to improve existing and future products Collaborate with engineering and product teams to productionize AI solutions and deliver customer-facing features Who You Are: You will graduate in 2027 with a degree in Machine Learning, Artificial Intelligence, Statistics, Computer Science, or a related field You have experience building machine learning models through coursework, research, internships, or personal projects You have strong programming skills and are eager to lear

The Incident Insights & Readiness SRE team at Datadog fosters a resilient culture by using incidents as learning opportunities and catalysts for growth. Our users are Datadog engineers, and we build the software, tooling, and operational frameworks that help them prepare for, respond to, and learn from incidents. We work closely with engineering teams across Datadog to analyze incidents and turn those insights into better tools, stronger incident response, and organizational learning. Our efforts empower Datadog to navigate unexpected failures confidently, efficiently, and with a commitment to continuous learning and systems improvement. At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You’ll Do: Own and improve the on-call experience for the company by establishing best practices and building platforms to support on-call rotations and compensation. Define how we respond to incidents, lead the design and implementation of software to streamline the process, and collaborate with product teams to improve incident response across Datadog. Our aim is to fully support our incident responders in dealing with complexity. Contribute to the post-mortem process for the company, collaborating with teams on writing them, and identifying opportunities to reduce friction and enhance learning value for the organization. Our team also runs a weekly postmortem reading group. Support various teams in facilitating incident reviews that emphasize learning and blamelessness. Help them share their learnings across the organization to improve the resilience of our people. Provide technical leadership and day-to-day coaching to team members, accelerating their growth through design reviews, collaborative problem-solving and operational excellence best practices. Tra

The Applied AI team designs and builds algorithmically driven features in the Datadog app. We work across a range of applications, primarily focusing on analysis on streaming data such as anomaly detection , error outliers and faulty deployment analysis . As an Applied Scientist you will work on building models and algorithms for machine learning powered features within the Datadog platform. You will work closely with our engineering and product partners to explore, build, scale and deliver these features that we incubate within the Applied AI team. At Datadog, we place value in our office culture - the relationships that it builds, the creativity it brings to the table, and the collaboration of being together. We operate as a hybrid workplace to ensure our employees can create a work-life harmony that best fits them. What You’ll Do: Design solutions for our different use cases. You will research and benchmark relevant algorithms to find the best fit for our use-cases Leverage machine learning algorithms and statistical techniques to build new scalable product features Develop, deploy and monitor new and existing features to production Participate in our journal club by reading and presenting the latest academic research papers to the team Explore, analyze and tell the story behind high volumes of data flowing through Datadog systems Maintain and monitor the models, services and infrastructure owned by your team Participate in your team’s on-call rotation Who You Are: You have a BS/MS/PhD in a Computer Science, Engineering, Machine Learning or related scientific field or equivalent experience You have experience working with high-scale systems and datasets including building models, applying machine learning to real business problems, and writing production data pipelines You can explain complex ideas and algorithms to non-technical audiences You care about code simplicity and performance You are excited to work on

Who are we? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Role Overview: The integration team is responsible for developing and scaling machine learning algorithms and infrastructure for LLM post-training, with a focus on large-scale, distributed RL methods. We strive for excellence in both engineering and science by meticulously designing experiments and design docs. While tasks are assigned according to everyone’s expertise, there is a global team effort to write production code and support the team research efforts, depending on individual interests and organizational needs. In particular, this role aims to enhance the global quality of the post-training codebase by implementing new tools to ease and support research, optimizing post-training algorithms, and scaling distributed RL to unprecedented levels. Please Note: We have offices in London, Paris, Toronto, San Francisco, New York but we are also remote-friendly! Applicants for this role may work anywhere between UTC−06:00 and UTC+01:00. Key Responsibilities: Design and write high-performing and scalable software for training models. Develop new tools to support and accelerate research and LLM training. Coordinate with other engi

As a Research Scientist on our team, you will partner with Research Engineers, working on fundamental research problems and collaborating with Datadog's product and engineering teams to translate research advances into products. Building on our track record of AI-powered solutions (e.g., Bits AI , Bits Evolve , and our time series foundation model ), Datadog AI Research tackles high-risk, high-reward problems grounded in real-world challenges in cloud observability and security. We are focused on two research areas: World Models for Observability -- Training multimodal foundation models that learn the joint dynamics of distributed systems across metrics, traces, logs, topology, and events. These models power advanced forecasting, anomaly detection, root cause analysis, counterfactual simulation ("what if?"), and provide a learned planning backbone for our autonomous agents. Trained Agents for Observability -- Post-training models to operate autonomously across Datadog's domain. SRE incident response is our first target, with a clear path to code repair, security response, and infrastructure optimization. We build the simulation environments, RL training loops, and evaluation infrastructure needed to train agents that match or surpass frontier models at a fraction of the cost. What You'll Do: Conduct research in generative AI and machine learning, building specialized foundation models and trained agents for observability Train multimodal models on large-scale, diverse telemetry data (metrics, logs, traces, topology, events) using distributed training infrastructure Design and build simulated environments and RL training loops for on-policy agent training and evaluation Collaborate with cross-functional teams (Product, Engineering) to integrate capabilities like multimodal world modeling and autonomous agents into Datadog's products Stay at the forefront of foundation models, world models, and RL-based agent research Contribute to r