Applied Machine Learning Engineer

ActAI; Portugal; Presencial; EUR 65 000 - 95 000; Tempo integral

Resumo da oferta

ActAI is building AI-native mobile applications and ML-powered features. We seek an engineer to build and improve ML systems powering our AI-native apps, focusing on classification, retrieval, ranking, and personalization.

You will own model development to production deployment, develop evaluation pipelines, embeddings-based search, and personalization models while balancing latency, cost and privacy.

Qualificações

• Strong foundations in machine learning and modern neural architectures.
• Hands-on production experience with ML models.
• Experience in classification, ranking, retrieval or personalization.
• Understanding of evaluation, experimentation and performance metrics.
• Able to write production-quality code with real-world datasets.
• Balance model quality, latency, cost and reliability.

Responsabilidades

• Build and improve ML models for classification, ranking, retrieval and personalization.
• Develop efficient classifiers and optimize accuracy, latency and cost.
• Improve search relevance and context retrieval using embeddings, hybrid search and reranking.
• Build personalization models that learn from user behavior while respecting privacy.
• Develop evaluation pipelines, labeled datasets and metrics to measure model quality.
• Fine-tune and adapt existing models when supported by sufficient data.
• Build and maintain ML training, inference and deployment pipelines.
• Debug production issues, monitor model performance and ship improvements based on real-world feedback.

Conhecimentos

ML foundations

Production ML

Classification

Ranking

Retrieval

Personalization

Evaluation metrics

Code quality

Ferramentas

Python

PyTorch

JAX

Embeddings

Vector search

Descrição da oferta de emprego

There are over 5 billion users using basic applications today such email, notes, tasks, calendar and they're not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting.

Our first application is an agent-first mobile communication app. We believe building applications that users use daily for communication, coordination and collaboration needs will help integrate AI into their lives. The net effect is to eliminate misinformation, achieve low coordination costs, enable high quality tasks and collaboration at scale with minimal or little prompting.

About

There are over 5 billion users using basic applications today such email, notes, tasks, calendar and they're not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting.

Our first application is an agent-first mobile communication app. We believe building applications that users use daily for communication, coordination and collaboration needs will help integrate AI into their lives. The net effect is to eliminate misinformation, achieve low coordination costs, enable high quality tasks and collaboration at scale with minimal or little prompting.

Role

Build and improve ML systems that power our AI-native applications.

Work on classification, retrieval, ranking, personalization and evaluation, taking ownership from model development to production deployment.

This role is for engineers who enjoy solving practical ML problems, improving model quality and shipping reliable systems used by real users.

Focus

• Build and improve ML models for classification, ranking, retrieval and personalization.
• Develop efficient classifiers and optimize model accuracy, precision, latency and cost.
• Improve search relevance and context retrieval using embeddings, hybrid search and reranking.
• Build personalization models that learn from user behavior while respecting privacy.
• Develop evaluation pipelines, labeled datasets and metrics to measure model quality.
• Fine-tune and adapt existing models when supported by sufficient data.
• Build and maintain ML training, inference and deployment pipelines.
• Debug production issues, monitor model performance and ship improvements based on real-world feedback.

Tech Stack

• Python
• PyTorch / JAX
• Embeddings, vector search and ranking models
• Production ML systems and inference pipelines

Ideal Experience

• Strong foundations in machine learning and modern neural architectures.
• Hands-on experience building and deploying ML models in production.
• Experience in classification, ranking, retrieval, recommendation systems or personalization.
• Understanding of model evaluation, experimentation and performance metrics.
• Comfortable writing production-quality code and working with real-world datasets.
• Able to balance model quality, latency, cost and reliability.
• Strong ownership, problem-solving skills

Outcomes

• ML models in production meet expected accuracy, relevance, latency and reliability targets.
• Classification and ranking improvements deliver measurable gains in model quality.
• Evaluation pipelines identify regressions and support reliable model releases.
• Training, inference and data pipelines are robust, reproducible and maintainable.
• Model improvements are driven by real-world feedback and measurable results.
• Collaborates effectively with engineering and product teams to deliver reliable ML-powered features.

How We Work

The best products in the world are built by small, world-class teams. We are a high-talent-density, hands-on team. We make decisions collectively and move quickly, balancing high-quality work with continuous learning.

Joining our team requires the ability to bring structure, exercise judgment and execute independently. Our goal is to put a truly magical product into the hands of our users.

Interview Process

If there appears to be a fit, we'll reach out to schedule 3, but no more than 4 interviews.

Applications are evaluated by our technical team members. Interviews will be conducted virtually and/or onsite.

We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us.