Machine Learning Engineer

• Help collect and categorize data from public sources as well as from our customer base (when authorized).
• Support training data workflows — we outsource annotation, but data still needs validation and sometimes transformation for the target network.
• Improve test performance and ensure compatibility with new hardware — not only in the NVIDIA ecosystem with the Jetson edge platform, but also on programmable FPGAs and server-grade CPUs (e.g., Intel).
• Test, improve, and tune existing algorithms (neural networks, Bayesian/Markov models, decision trees, reinforcement learning networks, Gaussian models, etc.).
• Port existing solutions (and training data) to different frameworks.

• Build MLOps infrastructure from the ground up at Vestiaire Collective
• Drive AI authentication initiatives focusing on luxury product authentication and counterfeit detection
• Scale foundational architecture across the entire marketplace
• Expand ML capabilities for search and recommendation systems
• Design robust, decoupled architectures and spearhead the MLOps strategy

AI Engineer

• Develop cutting-edge GenAI solutions, incorporating the latest techniques from Databricks AI research to solve customer problems
• Own production rollouts of consumer and internally facing GenAI applications
• Serve as a trusted technical advisor to customers across a variety of domains
• Present at conferences such as Data + AI Summit, recognized as a thought leader internally and externally
• Collaborate cross-functionally with the product and engineering teams to influence priorities and shape the product roadmap

AI Engineer – Forward Deployed Engineer

• Develop and train Neural Quantum States (NQS + VMC), with pretraining of the NQS on datasets generated by the QPU.
• Benchmark this approach against established numerical methods (e.g., exact diagonalization, standard VMC, tensor networks) and against raw QPU data.
• Apply NQS to represent observables and many-body wavefunctions of magnetic Hamiltonians.
• Contribute to internal tools and publications.

Machine Learning Internship – Rydberg-Based Quantum Simulators

• Build production agents — design, develop, and deploy goal-oriented AI agents and multimodal/conversational experiences using frameworks like Google ADK, Langchain, LangGraph, combined with orchestration tools like n8n where appropriate.
• Own the production lifecycle of what you ship — establish robust AIOps/AgentOps practices (monitoring, versioning of agent blueprints, evaluation pipelines, reliability) within your team's scope.
• Contribute to the shared Agentic Platform (Core pillar) — gateways, evaluation frameworks, observability, MaaS/AaaS APIs — so feature teams build faster on solid foundations.
• Build agent-side integrations (Feature pillar) — develop the MCP servers and backend services that agents need to interact with enterprise systems, in partnership with other R&D teams (and picking up the work yourself when a partner team doesn't have bandwidth).
• Be the Python referent in your team — own production-quality Python, enforce strong SWE principles (unit tests, CI/CD, Git, code review), and bring AIOps/MLOps best practices wherever you sit.
• Build a working expertise on agentic design patterns (eval, guardrails, multi-agent orchestration) and share it with AI champions and AI builders across the company as the GenAI Center of Expertise takes shape.
• Engage with stakeholders — talk to internal teams to understand operational pain points and translate them into measurable GenAI solutions. You don't lead cross-team architecture, but you should be credible across the org.

AI Engineer, Freelance

• Develop and train Neural Quantum States (NQS + VMC), with pretraining of the NQS on QPU-generated datasets.
• Benchmark this approach against established numerical methods (e.g., exact diagonalization, standard VMC, tensor networks) and against raw QPU data.
• Apply NQS to represent observables and many-body wave functions of magnetic Hamiltonians.
• Contribute to internal tools and publications.

Machine Learning Intern, Rydberg-Based Quantum Simulators

• Define and drive the product vision and roadmap for Scaleway’s MLOps offering
• Launch new MLOps products rapidly, starting with a managed MLflow solution
• Act as the “Voice of the Customer” during discovery and validation phases with data scientists and ML engineers
• Work closely with engineering teams to ensure fast and iterative product delivery
• Validate customer use cases and prioritize features based on impact and adoption
• Ensure strong integration between MLOps products and the broader Scaleway ecosystem
• Balance strategic product vision with hands-on operational execution
• Contribute to Scaleway’s AI and cloud strategy through customer-centric product development
• Collaborate with engineering and cross-functional teams to align roadmap and delivery priorities

Product Manager, MLOps

• Vous contribuerez à la conception et au développement de projet d'IA, et à leur industrialisation
• Vous contribuerez l'amélioration des bonnes pratiques MLOps: versioning, reproductibilité, etc...
• Vous travaillerez sur l’intégration d’outils et de briques dans notre plateforme MLOps

MLOps – Alternance

• Development and enhancement of AI applications
• Contribute to the development of the central AI service: new features, tools integration, robustness and performance
• Evolve the conversational agent and make its services easier to use by other applications
• Participate in data science projects: modeling, evaluation, and production deployment
• Ensure code quality and maintainability of solutions (tests, documentation, reviews) within Azure DevOps
• Capture and translate business requirements
• Engage with business teams to identify new needs and AI use cases
• Evaluate and implement solutions to these needs in a way that is coherent with the ecosystem
• Propose solutions adapted to cost, time, and complexity constraints
• Monitor developments in Azure AI models and services, assessing their relevance to our projects
• Contribute to improving team practices: performance, cost control, security

AI & Data Engineer

• Co-design with company executives (C‑level) the Data & AI vision for the clients we support.
• Translate that vision into technology roadmaps, target architectures and implementation strategies.
• Ensure alignment between business objectives, technical constraints and measurable value creation.
• Serve as the technical authority on projects: stack selection, architecture design, best practices, scalability and security.
• Oversee the design and implementation of bespoke solutions: data pipelines, cloud platforms, AI models, APIs, middleware and MLOps.
• Ensure technical consistency across projects: version upgrades, maintenance, performance and operational robustness.
• Lead multidisciplinary teams to design, develop and deploy end-to-end Data & AI solutions.
• Guarantee the technical quality, performance and reliability of delivered solutions.
• Stay at the forefront of technological advances: new algorithms, frameworks, architectures, AI/ML tools and innovations (e.g., Databricks, Snowflake).
• Help shape and scale the custom-build function to take it to the next level.
• Mentor, coach and upskill teams of data engineers, ML engineers, developers and data scientists.

Senior Tech Lead – AI Engineering

• Drive the research, development and validation of state-of-the-art Deep Learning architectures (Transformers, Temporal CNNs) for various time-series data from Cardiac Implantable Electronic Device (CIED) signals.
• Build and optimize Self-Supervised Learning pipelines to leverage our database of +100,000 patients.
• Explore the SNDS data via the Health Data Hub, unlocking new research opportunities at scale.
• Ensure the reproducibility, scientific correctness and transparency of each experiment, and medical relevance of our AI projects for both patients and caregivers.
• Contribute to scientific publications in collaboration with our Clinical team.

Senior Deep Learning Engineer

AI Research Scientist

• Lead the design and define the technical architecture of complex AI agents to automate internal processes (workflows, RAG, business assistants).
• Define the development and evaluation strategy for assistants (e.g., Copilot, Gemini) and intelligent agents, maintaining active technological watch.
• Conduct complex and autonomous data analyses to address strategic issues, from modeling to presenting recommendations to stakeholders.
• Architect and develop robust, scalable Machine Learning solutions (NLP, tree-based models, neural networks), ensuring performance and relevance of deployed models.
• Ensure industrialization and reliability of solutions through CI/CD pipelines and a high-performance MLOps infrastructure (monitoring, alerting, automated tests).
• Define and promote MLOps and Software Engineering best practices (Git, CI/CD, containerization).
• Mentor junior team members and support their professional development.

Senior AI Scientist / Engineer

• Build Scalable & Reliable Architecture
• Design and implement scalable, maintainable end-to-end features (specification, development, testing, documentation, support)
• Ensure resource consumption is optimal when necessary
• Drive architectural decisions that support long-term business growth
• Ensure clean code, automated testing, and CI/CD best practices
• Optimize APIs, data models, and microservices for performance
• Collaborate Cross-Functionally
• Partner with Product Managers to align business goals and technical feasibility
• Collaborate with DevOps to ensure stability and efficient deployments
• Facilitate technical discussions and drive knowledge-sharing across teams
• Improve System Performance & Reliability
• Continuously improve system performance, observability, and fault tolerance
• Apply technical refactoring efforts and reduce legacy risks
• Embed security and compliance into development practices
• Mentor & Lead Engineering Teams
• Act as a mentor and technical reference for engineers
• Set high standards for code quality, documentation, and development practices
• Drive Team Growth & Talent Development
• Take part in recruiting top talent and leading technical interviews
• Contribute to onboarding and continuous learning programs
• Foster a collaborative, inclusive, and high-performing engineering culture.

AI Engineer – Software Engineering

• You will integrate a small, high-ownership squad where you own the full lifecycle - design, implement, deploy, monitor, and iterate - with direct impact on revenue-critical systems processing millions of bid requests per second.
• Lead the design and architecture of backend services that power real-time model inference and bidding decisions for our OpenRTB platform.
• Collaborate with data scientists and machine learning engineers to deploy, monitor, and optimize ML models that influence real-time bidding strategies, pricing decisions, and targeting - including potentially developing proprietary models in-house.
• Oversee the development of A/B testing frameworks and ensure the seamless integration of experimentation tools into our platform for continuous model and bidding optimization.
• Ensure that all backend services are high-performance, low-latency, and scalable, capable of handling large data volumes (millions of bidding events per second).
• Set best practices for architecture, API design, and distributed systems to ensure robust and maintainable systems at scale.
• Work with the cloud infrastructure teams to ensure efficient deployment, scaling, and monitoring of backend services using Kubernetes, Docker, and CI/CD pipelines.
• Work closely with product managers to define and implement new features, optimizations, and improvements to the bidding and model inference system.
• Lead efforts to optimize performance and cost-efficiency across the backend infrastructure, ensuring that the system can scale effectively with increasing traffic and data.
• Continuously monitor the system’s performance, perform post-deployment analysis, and make improvements based on real-world usage and A/B test results.

Senior ML Engineer

• Advance optimal control strategies: Develop methods to improve the optimization of quantum gates and state-preparation protocols under realistic noise, dissipation, and hardware constraints.
• Explore RL-based control approaches: Formulate selected adaptive control or sequential experiment-design tasks as reinforcement learning problems, and evaluate when RL offers benefits over more structured physics-based methods.
• Design adaptive experiments: Build strategies that use measurement history and model uncertainty to choose the next most informative experiment or control setting.
• Study parameter sensitivity and identifiability: Leverage differentiable simulators and open-system models to understand which experiments best constrain key physical parameters.
• Support hardware integration: Account for practical constraints such as inference speed, transfer latency, and compilation time when designing methods for laboratory use.
• Cross-Functional Collaboration: Partner closely with physicists and ML researchers to interpret experimental data and translate complex physical requirements into robust software solutions.

Machine Learning and Optimal Control Intern

• Collaborate with stakeholders (product, marketing, operations, commerce, logistics) to analyze business challenges, define precise problem statements, and model them as tractable machine learning tasks (e.g., defining classification vs. ranking objectives, specifying metric targets).
• Design, implement, and maintain scalable machine learning pipelines, emphasizing Large Language Models (LLMs) and generative architectures.
• Fine-tune custom LLMs for specific use cases for cost and performance optimizations.
• Monitor AI systems performance in real time (latency, accuracy, drift) and establish automated retraining processes to maintain high quality, robustness, and fairness.
• Partner with PMs, UX/UI designers and frontend/backend engineers to embed AI capabilities into Veepee’s Information System.

Senior AI Engineer

• Deliver end-to-end ML use cases (recommendation, safety algorithms, etc.).
• Ensure production readiness, scalability, and long-term maintainability.
• Balance speed of delivery with robustness and reliability.
• Define and improve the full ML lifecycle (training, deployment, monitoring, iteration).
• Establish KPIs and monitoring standards to track model performance over time.
• Ensure continuous alignment with product and safety objectives.
• Contribute to the “ML as a Platform” strategy (tools, workflows, reusable components).
• Define scalable standards for ML development across teams.
• Enable self-service ML capabilities.
• Take ownership of legacy models and realign them with current business needs.
• Improve, retrain, and integrate them into modern pipelines.
• Define standards for LLM usage (moderation, recommendation).
• Explore and implement advanced ML approaches where relevant.
• Partner with Data Engineering, MLOps, Backend Platform, and Product teams.
• Act as a bridge between ML, platform, and business stakeholders.
• Bring technical leadership and structure to ML practices across the organization.

Staff ML Engineer

LLM Engineer

• Design, implement, and own large-scale cloud-based HPC and MLOps platforms
• Architect multi-environment clusters (AWS, GCP, Azure)
• Lead the development of developer and cloud platforms
• Design, implement, and manage unified platform catalogs
• Build and maintain Python-based automation frameworks
• Operationalize ML model serving at scale
• Mentor engineers and lead cross-functional teams for integrated solutions

See all jobs on JobTailor

Machine Learning Engineer

Machine Learning Engineer

AI Engineer – Forward Deployed Engineer

Machine Learning Internship – Rydberg-Based Quantum Simulators

AI Engineer, Freelance

Machine Learning Intern, Rydberg-Based Quantum Simulators

Product Manager, MLOps

MLOps – Alternance

AI & Data Engineer

Senior Tech Lead – AI Engineering

Senior Deep Learning Engineer

Senior AI Scientist / Engineer

AI Engineer – Software Engineering

Senior ML Engineer

Machine Learning and Optimal Control Intern

Senior AI Engineer

Staff ML Engineer

Staff Engineer, AI Infrastructure – MLOps