AI Jobs in Paris

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As a Deployment Strategist Lead, you will be fully responsible for opening up a new market for ElevenLabs, building and leading a team to achieve this. Your duties include meeting with strategic customers to understand their critical audio and voice AI needs and pain points, building and leading a team of forward deployed engineers within the region, identifying relevant use cases by deeply engaging with customer problems and workflows, and working with engineers to implement voice and audio AI technology into innovative solutions. You will design and architect bespoke integrations for customers to ensure seamless technology fit, guide customers on best practices for implementing voice and audio AI models, present results and proposals to audiences ranging from technical teams to executives, collaborate with research and product teams to incorporate field insights into software products and AI models, build and deliver demos of voice and audio AI technology, scope potential applications in new industries, expand AI solutions globally, take full ownership of major projects for strategic partners and work hands-on to deliver high-impact solutions, and collaborate daily with customer engineering and executive teams to ensure optimal technology implementation.

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As a System Architect, you will own the end-to-end architecture, system definition, and strategic implementation for the entire portfolio of robotic systems, collaborating closely with executive leadership, technical leads, and the Product Manager to ensure efficiency. Responsibilities include translating complex strategic goals into global system-of-systems designs and defining the overall system architecture strategy across the enterprise. You will ensure all systems meet defined needs through verification of scope, complex simulations, and precise system sizing to guide major technical investments. Coordination and technical leadership involve managing large multidisciplinary engineering organizations and providing overarching technical leadership across cross-functional design efforts to ensure long-term performance, robustness, and strategic reliability. Additionally, you will govern system integration standards and validation processes, manage specification by ensuring architectural prerequisites are met, and drive multi-system architecture reviews for enterprise design consistency. You will also implement and institutionalize processes to enhance requirements traceability, system documentation standards, and validation workflows across the engineering organization.

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As an AI Engineer at Maki, your responsibilities include designing and implementing end-to-end AI features that power the company's HR agents, ranging from prototyping to production. You will experiment with large language models (LLMs) by testing, fine-tuning, and evaluating both open-source and commercial models to achieve the best balance of performance, cost, and safety. Additionally, you will develop evaluation pipelines to measure the accuracy, reliability, bias, and business impact of AI-driven features. Optimizing AI features for scalability by addressing latency, caching, and cost-efficiency to make them enterprise-ready is also part of your role. You must ensure the safety and compliance of AI features by collaborating with security and product teams to meet enterprise standards for privacy, fairness, and reliability. Collaboration with cross-functional teams including product managers, designers, and engineers to convert ideas into shipped features is essential. Lastly, staying up to date with new AI research, tools, and frameworks to maintain Maki's position at the cutting edge is expected.

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As an AI Deployment Engineer, you will serve as the primary technical subject matter expert post-sale for a portfolio of customers, embedding deeply with them to design and deploy Generative AI (GenAI) solutions. You will engage with senior business and technical stakeholders to identify, prioritize, and validate the highest-value GenAI applications in their roadmap, and accelerate customer time to value by providing architectural guidance, building hands-on prototypes, and advising on best practices for scaling solutions in production. You will maintain strong relationships with leadership and technical teams to drive adoption, expansion, and successful outcomes. Additionally, you will contribute to open-source resources and enterprise-facing technical documentation to scale best practices across customers, share learnings and collaborate with internal teams to inform product development and improve customer outcomes, and codify knowledge and operationalize technical success practices to help the Solutions Architecture team scale impact across industries and customer types.

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The Senior ML Operations (MLOps) Engineer at Eight Sleep is responsible for introducing and implementing cutting-edge ML technologies, owning the design and operation of robust ML infrastructure including scalable data, model, and deployment pipelines to ensure reliable model delivery to production. They collaborate cross-functionally with R&D, firmware, data, and backend teams to ensure reliable and scalable ML inference on Pods. They optimize ML systems for cost, scalability, and performance across training and inference, and develop tooling, microservices, and frameworks to streamline data processing, experimentation, and deployment. The role requires effective communication in a remote work environment.

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Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for diverse use cases. Deploy and operate the core ML inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture that improve performance, latency, throughput, and efficiency of deployed models. Build tools to identify bottlenecks and sources of instability and design and implement solutions to address the highest priority issues.

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Design algorithms that decompose high-level missions into structured, solvable guidance tasks for autonomous robots. Develop and optimize mission and path-planning frameworks for autonomous systems. Build scalable backend integrations for mission guidance and execution. Run simulations and validation campaigns to assess autonomy consistency across diverse mission types. Partner with AI/ML, backend, and product teams to ensure algorithms are efficient, testable, and deployable in real-time environment.

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The Forward Deployed Engineer (FDE) is responsible for designing and shipping production systems around models, including owning integrations, data provenance, reliability, and on-call readiness across research, clinical, and operational workflows. They lead discovery and scoping from pre-sales through post-sales, translating ambiguous workflow needs into hypothesis-driven problem framing, system requirements, and execution plans with measurable endpoints. They define and enforce launch criteria for regulated contexts, including validation evidence, audit readiness, and outcome metrics, driving delivery until sustained production impact is demonstrated. The role involves building in sensitive scientific data environments shaped by auditability, validation, and access controls, and running evaluation loops to measure model and system quality against workflow-specific scientific benchmarks, using results to drive model and product changes. The engineer distills deployment learnings into hardened primitives, reference architectures, validation templates, and benchmark harnesses that scale across regulated life sciences environments.

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As a Forward Deployed Engineer at Dust, your responsibilities include writing production-quality code to build custom integrations, APIs, and tooling for enterprise customers where off-the-shelf solutions are insufficient. You will contribute features and improvements directly to the Dust platform based on customer requirements and field insights. You act as a key cross-functional partner by collaborating with Sales to help onboard customers and with Customer Success to ensure users maximize the value of Dust. You help set the product roadmap by surfacing feedback and insights from customers, partnering with Design and Engineering. You lead demo calls, communicate Dust's value proposition to buyers and evaluators, and act as a trusted advisor to strategic customers by helping set up their Dust workspace, data connections, AI assistants, and workflows. You identify and highlight successful use cases and craft content to help users maximize Dust's value. Additionally, you lead workshops and training sessions to demonstrate advanced features and facilitate customer access to advanced use-cases through Dust's Developer platform and API.

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The AI Safety Engineer is responsible for designing and building scalable backend infrastructure for content moderation, abuse detection, and agents guardrails by deploying AI/ML models into production systems. They will architect robust APIs, data pipelines, and service architectures to support real-time and batch moderation workflows. The role includes implementing comprehensive monitoring, alerting, and observability systems, establishing SLIs, SLOs, and performance benchmarks. The engineer will collaborate with ML engineers to translate research models into production-ready systems and integrate them across the product suite. Additionally, they will drive technical decisions and contribute to the vision for the safety roadmap to build next-generation platform guardrails for scale and precision.