Top Remote Jobs in Lyon

Develop and improve semiconductor engineering software across UI, optimization workflows, C++ codebases, and AI integrations. Build MCP servers and agent pipelines that expose simulation tools to LLMs, while improving usability and reliability for scientists and technical users. The role also offers opportunities to launch and lead new software projects end-to-end. No prior simulation or optimization experience is required.

Build and grow Cloudera’s sovereign cloud business across EMEA. Develop relationships with sovereign cloud providers and enterprise customers, create marketplace offerings, drive partner enablement and sales motions, generate pipeline, and manage regional forecasts and bookings. The role requires expertise in enterprise cloud, data, AI/ML, sovereignty regulations, complex multi-stakeholder deals, executive communication, and cross-functional collaboration.

Manage and convert inbound EMEA SaaS leads (trials, enquiries, quotes, RFPs). Qualify and prioritise opportunities, demo product, recommend solutions, maintain CRM (HubSpot), follow up to close, and share customer/market insights with the team.

Lead and grow a backend engineering team operating with AI-assisted development. Own delivery quality and predictability, hiring, performance management, technical direction, and team development. Partner closely with Product, engage directly with freight customers, and help establish effective AI engineering practices, agent guardrails, review standards, and autonomous workflows. Remain hands-on approximately one day per week while improving engineering processes and outcomes across a distributed, remote-first organization.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

Designs, builds, operates, and scales Sezzle’s AWS and Kubernetes infrastructure. Responsibilities include improving performance, reliability, resilience, observability, disaster recovery, cost efficiency, and database scalability; participating in on-call incident response; implementing infrastructure-as-code and automation; conducting migrations and recovery testing; and developing AI-assisted SRE tooling.

Build and own the architecture for Dune’s curated data lifecycle, including dependency-aware orchestration, backfills, schema evolution, retries, recovery, data quality, and alerting. The role combines backend software engineering with large-scale data systems work across Go, Kotlin, Rust, Python, and SQL. Responsibilities include designing ingestion and curation contracts, making architecture decisions, solving ambiguous problems, and leading work that other engineers can execute.

Manages day-to-day execution of influencer marketing campaigns, coordinating creators, clients, vendors, and internal teams. Responsibilities include campaign activation, content and community management, data collection, reporting, client communications, production support, risk escalation, pitch materials, planning, and workflow optimization using AI and automation tools.

Provide technical customer support for Ashby by diagnosing integration and API issues, troubleshooting via logs and scripting, collaborating with engineering on escalations, creating documentation, and driving product improvements from customer feedback.

Lead the company’s Physical AI research and engineering function as a hands-on technical leader. Define research priorities, design and train Transformer-based models, establish rigorous offline and real-world evaluations, integrate systems with physical robots, and analyze failures across data, models, hardware, and environments. Build a focused team, guide technical architecture and experiments, translate customer needs into requirements, and deliver measurable improvements through deployed models, benchmarks, evaluation systems, or data products.