Senior Machine Learning / NLP (Contractor)

RavenPack; Madrid; Presencial; EUR 60.000 - 100.000; Jornada completa

Descripción de la vacante

RavenPack in Madrid is seeking an experienced Machine Learning / NLP Engineer to lead a high-impact infrastructure project. You will design, evaluate, and deploy advanced ML models to enhance our large-scale detection pipeline, working with core engineering and data teams.

The role focuses on modern NLP techniques, low-latency inference, and seamless integration with existing systems while maintaining robust CI/CD for ML deployments.

Formación

• Deep theoretical and practical understanding of core NLP tasks.
• Experience with transformer-based models and encoding methods.
• Proven track record deploying ML models for low-latency, scalable systems.
• Experience designing ML evaluation frameworks and datasets.
• Experience combining probabilistic models with legacy software; A/B testing.
• Strong coding skills, documentation, and CI/CD for ML deployments.

Responsabilidades

• Design, train, and deploy NLP models to complement and scale our existing high-throughput data architecture.
• Architecture Integration: Build robust integration layers that allow new ML predictions to work with our production systems.
• Continuous Improvement Pipelines: Develop automated feedback loops for systems that learn and adapt over time.
• Production Deployment & Optimization: Transition models from research to production with low latency and reliability.
• Quality & Evaluation Frameworks: Establish benchmarks, automated testing and monitoring for ML implementations.

Conocimientos

Advanced NLP

Transformer Models

MLOps

Evaluation Metrics

System Integration

Software Engineering

Descripción del empleo

About Us

At RavenPack, we are at the forefront of developing the next generation of generative AI tools for the finance industry and beyond. With 23 years of experience as a leading big data analytics provider for financial services, we empower our clients—including some of the world's most successful hedge funds, banks, and asset managers—to enhance returns, reduce risk, and increase efficiency by integrating public information into our models and workflows. Building on this expertise, we are launching a new suite of GenAI and SaaS services, designed specifically for financial professionals.

About Us

At RavenPack, we are at the forefront of developing the next generation of generative AI tools for the finance industry and beyond. With 23 years of experience as a leading big data analytics provider for financial services, we empower our clients—including some of the world's most successful hedge funds, banks, and asset managers—to enhance returns, reduce risk, and increase efficiency by integrating public information into our models and workflows. Building on this expertise, we are launching a new suite of GenAI and SaaS services, designed specifically for financial professionals.
Join a Company that is Powering the Future of Finance with AI
RavenPack has been recognized as the Best Alternative Data Provider by WatersTechnology and has been included in this years Top 100 Next Unicorns by Viva Technology. RavenPack has launched Bigdata, our Gen-AI platform tailored for finance, which is already being recognized as the #1 platform for powering financial AI agents.

European Legal Working Status Is Required.

Project Scope & Core Responsibilities

We are looking for an experienced Machine Learning / NLP Engineer to lead a high-impact, transformational infrastructure project. You will be responsible for designing, evaluating, and deploying advanced machine learning models to complement and enhance our large-scale, high-precision detection pipeline.
In this role, you will work closely with our core engineering, data, and technical leadership teams to ensure the seamless integration of new ML technologies with our existing systems.
The core objective of your role is to introduce modern, state-of-the-art ML approaches that run alongside our existing infrastructure to create a powerful, hybrid architecture. This is a unique opportunity to tackle complex challenges in low-latency inference, massive-scale entity resolution, and continuous learning systems.
Note: Due to the highly proprietary nature of our systems, the specific deliverables, architecture, and exact technologies will be discussed in detail under an NDA during your first interview.

Key Responsibilities

• System Enhancement & Innovation: Design, train, and deploy state-of-the-art Natural Language Processing (NLP) models to complement and scale our existing high-throughput data architecture.
• Architecture Integration: Build robust integration layers that allow new machine learning predictions to work seamlessly alongside our established production systems.
• Continuous Improvement Pipelines: Develop automated feedback loops and learning mechanisms that enable our systems to adapt, update, and improve over time with minimal manual intervention.
• Production Deployment & Optimization: Transition models from research/evaluation into full production, optimizing complex models for strict low-latency inference and high reliability.
• Quality & Evaluation Frameworks: Establish rigorous evaluation benchmarks, automated testing harnesses, and monitoring dashboards to guarantee the accuracy and stability of ML implementations.

Revised Required Skills & Experience

• Advanced NLP & Deep Learning: Deep theoretical and practical understanding of core NLP tasks, such as entity extraction, text classification, and contextual understanding.
• Transformer Architectures: Proven experience training, fine-tuning, and deploying modern transformer-based models and encoding methods for complex text tasks.
• MLOps & Production Engineering: Strong track record of optimizing models for low-latency environments (e.g., batch inference, model distillation, hardware acceleration) and deploying them at scale.
• Evaluation & Metrics: Expertise in designing ML evaluation frameworks, building gold-standard baseline datasets, and tracking performance metrics to identify and resolve edge cases.
• Complex System Integration: Experience combining probabilistic machine learning models with deterministic or legacy software architectures, including confidence scoring and A/B testing rollouts.
• Software Engineering Best Practices: Strong coding skills with a focus on writing clean, scalable code, creating technical documentation, and maintaining robust CI/CD pipelines for ML models.

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.