Top Machine Learning Jobs in Lille

Lead product strategy and roadmap for query interpretation and search quality. Own models, tools, and metrics to optimize recall and precision. Collaborate with ML, infra, and product teams, triage quality issues, benchmark competitors, and build evaluation frameworks and explainability for search results.

Lead the Recommendations engineering team to build scalable, reliable recommendation-as-a-service products. Own recommendation quality and business impact, set technical direction, translate product goals into engineering plans, ensure SLAs, hire and coach engineers, and drive system reliability and scalability.

Sell NVIDIA software solutions across assigned EMEA regions: build executive relationships, co-sell with account and channel partners, drive software revenue, manage pipeline/forecasting in SFDC, communicate technical value for virtualized datacenters, cloud-native and AI/ML workloads, coordinate internal resources, and support international travel.

Design and develop AI-driven features to enhance customer experience, execute ML projects, and optimize AI performance while collaborating cross-functionally.

The Head of Education & Community at Siena is responsible for developing product education, onboarding programs, community events, and market education resources. This role involves creating curricula, conducting workshops, and supporting sales with training and demos while leveraging AI for teaching efficiency.

The Sales Account Manager will drive revenues from new accounts, develop partnerships, manage forecasting, and collaborate with teams to close deals.

The role involves designing and developing model evaluation pipelines, user studies for subjective evaluations, and automated dashboards to report results, alongside leading the evaluation team and communicating with other teams to improve model performance.

The Head of Computer Vision will lead a team of ML Engineers, drive the long-term ML strategy, focus on breakthrough innovations, and establish cross-functional partnerships while mentoring and scaling the team effectively.

Lead a team of ML Engineers to develop and optimize ML systems, ensuring high-quality clinical care through AI. Foster collaboration across teams for innovative AI/ML features.

Lead the Weaviate Database product: own roadmap and strategy, translate customer and research insights into features across schema, storage, replication, and search quality, coordinate cross-functional teams, drive developer adoption, and measure feature outcomes post-release.

Lead product vision and discovery for a genomics-driven diagnostic and personalization platform. Validate hypotheses with patients, clinicians, and partners; translate research into product specs; coordinate labs and data providers; partner with engineers and researchers to build AI prototypes; shape data, regulatory, and GTM strategy while making core product and architectural decisions.

Build core components of a diagnostic and personalization platform: backend data pipelines, frontends, services for genomic data ingestion, ML model integration, APIs, and cloud infrastructure. Collaborate with product and ML teams, iterate on prototypes and MVPs, and make architectural decisions supporting scalability and reliability.

Dragonfly is seeking Software Engineers for various portfolio companies, focusing on crypto technologies. Engineers of all experience levels are needed across multiple stacks.

The Solutions Engineer will support customer agencies with the implementation of Cellebrite's digital intelligence platform, focusing on solution design, customer needs analysis, and risk identification while collaborating across teams.

Lead AI and digital workplace product strategy for an enterprise B2B SaaS platform. Conduct customer discovery, define roadmaps, write product specifications, prioritize features, and partner with Engineering, Design, Sales, and Customer Success to launch and measure AI-powered workplace experiences that improve knowledge access, productivity, collaboration, and automation while ensuring governance and security.

Lead product strategy and execution for AI-powered workplace experiences. Drive discovery, customer research, roadmap definition, and cross-functional delivery. Translate customer insights into product requirements, prioritize features balancing impact and feasibility, and measure adoption and business outcomes. Support GTM, enablement, and stakeholder alignment while monitoring AI and workplace technology trends.

Lead product strategy and execution for AI-powered workplace experiences. Drive discovery, define roadmaps and requirements, collaborate cross-functionally, support GTM and measure adoption and business impact.

Lead product strategy and execution for AI-powered workplace experiences. Drive discovery, define roadmap and requirements, collaborate cross-functionally, support GTM, measure adoption and business impact, and ensure secure, governed enterprise deployments.

Lead product strategy and execution for AI-powered workplace experiences. Own discovery, roadmaps, specs, cross-functional delivery, customer validation, GTM enablement, and success metrics for enterprise SaaS products.

The internship involves deploying data processing pipelines, collaborating with clients, conducting R&D in AI and data science, and supporting sales efforts.

As an Engagement Manager, you'll ensure customer success in protein R&D by building relationships, tracking metrics, and leading cross-functional teams. You'll manage enterprise accounts and advocate for customers while aligning their needs with Cradle's offerings.

Lead the estimating product roadmap for an AI-powered construction SaaS: prioritize and ship MVPs, run rapid experiments, conduct user discovery with estimators, define success metrics, and iterate using data and user feedback.

Lead development, validation and governance of GLMs across European markets; translate pricing models into portfolio steering, monitoring, dashboards and executive insights; act as AKUR8 technical authority; embed pricing governance and mentor pricing leads while collaborating with actuarial, data, underwriting and business stakeholders.

Lead development and productionization of TTS and voice-generation systems: train, fine-tune, evaluate models, deploy and optimize for inference, latency, scaling, monitoring, and reliability; translate research into production and define technical direction while collaborating with research, engineering, and product.

Own Go-To-Market for performance media buyers: build relationships, close and onboard clients, represent Kayzen at industry events, advise prospects on product strategy, and funnel market feedback across teams.