Top Machine Learning Jobs in Lisbon

Seeking a dotnet Engineer with 2+ years of experience in dotnet core, proficient in OOP, SQL, and ideally SPA frameworks like Angular or React. Role focuses on innovative development practices within a remote-first work environment.

The role involves working on high-quality, reliable, and scalable software solutions using various programming methodologies and technologies. You will embrace Agile practices and contribute to the company's mission to improve systems and deliver value to clients.

Embed with engineering clients to own full-cycle technical hiring for software, AI/ML, infrastructure and data roles. Run deep intakes, headhunt passive candidates, coach hiring managers on interview design and calibration, build hiring foundations, report on pipeline health, and use/builder recruitment AI agents to scale impact.

Build and maintain scalable backend systems that integrate real-time data, machine learning, and optimization models to optimize renewable and battery storage operations. Own architecture through deployment and maintenance, collaborate with engineers and data scientists, troubleshoot performance issues, and improve monitoring, reliability, and system performance.

Design, build, deploy, and maintain production-grade machine learning systems for a proactive AI platform. Own projects end to end, including data curation, model training, evaluation, inference, monitoring, troubleshooting, optimization, and continuous iteration. Translate research into reliable systems at scale while balancing latency, cost, safety, and performance. Collaborate with research, engineering, and product teams, mentor engineers, and provide rigorous technical reviews.

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.

Drive application security and regulatory compliance through vulnerability management, CI/CD and Azure-based practices, security scanning, compliance testing (DR, pen tests), and developer best-practice governance. Report on product compliance, lead technical change (GenAI, FinOps), and support remediation and patching efforts.

Develop, deploy, and maintain scalable AI/ML pipelines and production models, including LLMs and generative AI. Optimize model performance, latency, and cost across AWS, GCP, and Azure. Apply MLOps practices such as CI/CD, versioning, monitoring, logging, and maintenance. Collaborate with data scientists, software engineers, solution architects, and cross-functional teams to integrate AI capabilities into products and workflows.

Lead design and adoption of the Operations Target Operating Model, govern and deliver Ops improvement initiatives (including automation and AI), quantify and track cost-to-serve impact, coordinate multi-stakeholder M&A and growth activities, and produce COO-ready strategic outputs. Own KPI frameworks, initiative prioritization, and enablement of automation/AI use cases while coaching associates and ensuring TOM adoption across Operations.

Lead a product vertical end-to-end for a neobank superapp: define strategy, roadmap, and acceptance criteria; shape technical design for money movement, reliability, and compliance; apply and evaluate AI/LLM features; coordinate engineering, design, compliance, ops and finance to ship secure, auditable financial products.

Drive innovation in post-training methodologies for AI models, focusing on agentic behaviors, tool use, and enhancing model capabilities for real-world applications.

As an AI Research Engineer, you'll optimize model deployment and inference architectures for AI systems, enhancing performance across various applications, especially on resource-constrained devices. Responsibilities include designing efficient serving architectures, conducting inference tests, and collaborating with teams to integrate solutions.

As an AI Research Engineer, you will develop and optimize vision-language models, managing the full model lifecycle from training to deployment, focusing on efficiency and innovation in multimodal architectures.

Design, build, and maintain enterprise-grade UiPath automations including orchestration, exception handling, monitoring, security, and integrations. Translate architectures into reusable frameworks, review code, troubleshoot across environments, collaborate with architects and DevOps, and mentor junior engineers while supporting delivery planning and technical client discussions.

Design, build, and maintain enterprise UiPath automations including orchestration, exception handling, monitoring, security, and integrations. Translate architectures into reusable frameworks, review code, troubleshoot issues, mentor engineers, and support delivery in Agile environments.

Lead end-to-end ML work: define product problems as ML tasks, prototype and ship models (ranking, retrieval, LLM agents), build evaluation and monitoring, and measure marketplace impact (bookings, retention, GMV). Collaborate with engineering, product, and growth to deploy production models and improve LLM reliability.

As an AI/ML Engineer, you will develop and optimize machine learning models for mobile applications, collaborate with teams, and analyze data to enhance user experience.

Build and productionize large-scale TTS and adjacent speech models end-to-end: model architecture, training, alignment, evaluation, data curation, distributed GPU scaling, latency/cost profiling, tooling, and safety/mitigation for speech systems.

Senior technical lead embedding with customer teams to design, architect, and deliver production-grade AI/ML solutions. Drive model optimization, high-performance training (TPUs), efficient serving (vLLM), MLOps, agentic systems, GDPR-compliant private AI, ROI and cost optimization, pre-sales technical support, mentoring, and external thought leadership.

The Principal AI/ML Scientist & Engineer will lead a team, focusing on AI/ML strategy and developing data products for business outcomes.

The ML Ops Engineer will build and operate scalable ML inference platforms, focusing on model serving infrastructure and deployment pipelines for AI applications.

Responsible for building, deploying, and maintaining scalable machine learning solutions, implementing end-to-end ML pipelines, and ensuring model production quality.

Responsible for building, deploying, and maintaining scalable machine learning solutions, ensuring the transition from prototypes to production. Duties include designing ML pipelines, managing automated CI/CD processes, fine-tuning model performance, and collaborating with Data Scientists.

Lead benchmarking and profiling of GPU platforms for ML/AI workloads. Profile system and kernel-level GPU performance, debug and optimize ML training and inference, perform acceptance testing of GPU clusters, run experiments across GPU configurations and interconnects, and develop tooling and dashboards to visualize performance and bottlenecks.

Design, deploy, and maintain scalable ML pipelines and infrastructure. Build CI/CD for models, automate deployment/monitoring/rollback, implement model performance monitoring and feedback loops, integrate models with data teams, and provide technical leadership and mentorship.