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Engineering Excellence Engineer 2 in France

62 active opportunities · Updated October 2026

Explore current engineering excellence engineer 2 jobs across France. Filter by work mode, employment type, experience, department, date posted and distance.

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📍 France· Full-time
✓ Quality checked

Who Are We? Postman is the world’s leading API platform, used by more than 45 million+ developers and 500,000 organizations, including 98% of the Fortune 500. Postman is helping developers and professionals across the globe build the API-first world by simplifying each step of the API lifecycle and streamlining collaboration—enabling users to create better APIs, faster. The company is headquartered in San Francisco and has offices in Boston, New York, Austin, Tokyo, London, and Bangalore - where Postman was founded. Postman is privately held, with funding from Battery Ventures, BOND, Coatue, CRV, Insight Partners, and Nexus Venture Partners. Learn more at postman.com or connect with Postman on X via @getpostman. P.S: We highly recommend reading The "API-First World" graphic novel to understand the bigger picture and our vision at Postman. About the Role We're building out the Customer Success Engineering team at Postman, and we need business-minded engineers who can solve real problems at scale. You will partner closely with Customer Success Managers and own the “How” of technical solutioning. This is a consultative role that requires both depth and versatility. This is no support role; you'll be the technical architect of Customer Success, turning complex and nuanced challenges into repeatable solutions that benefit our entire enterprise customer base. As a CSE, you consult on the technical implementation path. When a customer needs to get from point A to point B, you figure out how to make it happen - whether that means architecting workflows within Postman, guiding integrations, or creating pragmatic workarounds when the product doesn't quite fit. A key part of this role is turning individual solutions into reusable assets that shorten time to value for future customers. We don’t believe in reinventing the wheel each time. Success in this role requires a blend of technical expertise, systems thinking, and the ability to translate complex business objectives into

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📍 France· Full-time
✓ Quality checkedCompany trend -94%

As a Research Engineer on our team, you will partner with Research Scientists to turn research ideas into working systems, building the data, tooling, and infrastructure that enable rapid iteration, trustworthy evaluation, and a smooth path from prototype to production. 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: Build and operate multimodal data pipelines, training and evaluation infrastructure, benchmarks, and internal tooling Implement models, run experiments at scale, and profile for reliability, performance, and cost Build simulation environments and replay infrastructure for agent training and evaluation Orchestrate distributed training and distributed RL with Ray, including scheduling, scaling, and failure recovery Establish rigorous automated benchmarks and regression tests for world model predictions, agent performance, and simulation fidelity Collaborate with Research Scientists, Product, and Engineeri

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