📍 Location: eu

🏷 Type: Not specified

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Senior DS/ML Engineer – Remote (Portugal – Lisbon) | Visa + Salary Insights

Job Overview

This Senior-Level DS/ML Engineer role at Veeva Systems centers on building production-grade AI/ML systems within the life sciences intelligence domain. You will design and scale NLP, NLU, and NLG-driven applications that power data products used in drug development and medical insights. The ideal candidate has 5+ years experience in machine learning, MLOps, and distributed systems, with proven ability to deliver real-world ML solutions from experimentation to deployment.

🌍 Work Eligibility & Location

This is a Remote role under a flexible Work Anywhere policy, allowing engineers to operate from home or office environments. While visa sponsorship is not clearly stated, candidates with existing EU work authorization are strongly positioned. The team operates in distributed mode but expects overlap with European time zones to maintain execution speed and collaboration efficiency.

💰 Salary Intelligence

The absence of an official salary is typical for global SaaS firms, but market benchmarks for Senior ML Engineers in Europe suggest a range of €80K to €115K. Given Veeva’s strong financial position and enterprise client base, compensation is likely competitive, especially for candidates with expertise in MLOps, NLP systems, and GenAI. Equity or performance incentives may further enhance the package.

📊 Role Breakdown

This position requires strong execution across the ML lifecycle. Approximately 40% of the role focuses on building machine learning models using Python and modern frameworks, particularly in NLP, NLU, and NLG. Around 30% involves designing and maintaining MLOps pipelines using tools such as MLflow, Kubeflow, and AWS SageMaker, ensuring scalability and reliability. The remaining 30% is dedicated to system architecture and product integration, translating models into user-facing SaaS applications.

You will develop data pipelines, optimize models for throughput and uptime, and deploy production services that directly influence customer workflows. A major emphasis is placed on end-to-end ownership, requiring engineers to validate, monitor, and continuously improve models. The role also includes work on entity resolution, recommendation systems, and intelligent automation within healthcare datasets, making it highly applied and impact-driven.

🧩 Required Skills & Fit

📈 Difficulty & Competitiveness

This is a high difficulty role requiring 5+ years of applied experience in both modeling and deployment. The expectation of end-to-end ML ownership significantly increases the bar, as candidates must demonstrate impact beyond experimentation. With global remote competition, only engineers with proven production experience and strong system design capabilities are likely to succeed in the hiring process.

🚀 Career Impact

This role offers strong career acceleration through exposure to production AI systems in healthcare, a high-impact and regulated domain. Engineers gain deep expertise in scalable ML infrastructure and develop skills that translate into Staff-level or AI leadership positions. The combination of domain specialization and advanced technical ownership significantly strengthens long-term career trajectory.

📋 Key Responsibilities

You will design, build, and deploy machine learning models using Python, focusing on NLP, NLU, and NLG applications. Core responsibilities include building scalable data pipelines, developing intelligent workflows, and optimizing model performance for production environments.

You will also implement and manage MLOps systems using MLflow, Kubeflow, or AWS SageMaker, ensuring continuous deployment, monitoring, and improvement. Additional tasks include experiment tracking, validating model accuracy, and collaborating with cross-functional teams to integrate AI capabilities into SaaS products. The role demands consistent delivery of reliable, scalable ML services that impact real-world user workflows.

🎯 Application Strategy

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🧠 Fit & Positioning Analysis

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📅 Application Signals

There is high urgency due to ongoing expansion of AI-driven healthcare products. The competition is strong given the global remote nature of the role and the demand for senior ML engineers with production experience. Candidates who apply early and demonstrate clear ownership of deployed ML systems will significantly improve their chances of progressing through initial screening stages.

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✅ Job Source & Verification

This job is sourced from Veeva Systems official careers page, ensuring high source credibility and accuracy of role details. All responsibilities and requirements reflect the employer’s direct listing. Last updated: April 2026. Candidates are advised to verify the role status on the official website before applying to ensure availability.