Machine Learning Engineer
TechJobs.ie Job Insights
At a glance
Technologies & skills
What you'll be doing
Contract Machine Learning Engineer role in Dublin (hybrid, Ireland). Join an established ML team to research and apply NLP, ML, and deep learning to business documents. Build, extend, and deploy NLP models for tasks like NER, parsing, classification, and clustering.
- Research and apply NLP, ML, and deep learning methods to business documents
- Build and extend NLP models for tasks such as named entity recognition, parsing, classification, clustering, and text prediction
- Deploy models to production
- Improve processes for maintaining training data and retraining models
Key requirements
Must-have
- Experience designing, building, deploying, and monitoring ML and deep learning solutions
- Proficiency in Python
- Technical experience with PyTorch, TensorFlow, spaCy, scikit-learn or similar frameworks
- Solid grasp of core ML concepts: training, validation, testing, precision/recall, bias/variance
Nice-to-have
- Experience building and deploying sequence-based deep learning and transformer models
- Experience extracting, cleaning, and working with large structured and unstructured datasets
- Exposure to computer vision
- Exposure to C#, Java, or C/C++
Role signals
- Technical focus
- machine learning, NLP, deep learning
- Hands-on vs management
- Hands-on
Similar jobs
Full job description
I'm currently recruiting for a Machine Learning Engineer based in Dublin. This is an initial 6-month contract. Focusing on hybrid working, strong day rate available.
About the Role
-
Join an established ML team , researching and applying NLP, ML and deep learning methods to complex business documents.
-
Build and extend NLP models for tasks such as named entity recognition, parsing, classification, clustering and text prediction.
-
Deploy models to production and improve the processes for maintaining training data and retraining models.
Requirements
-
Previous experience designing, building, deploying and monitoring ML and deep learning solutions.
-
Proficiency in Python and technical experience with PyTorch, TensorFlow, spaCy, scikit-learn or similar frameworks.
-
Solid grasp of core ML concepts: training, validation, testing, precision/recall and bias/variance.
Desirable
-
Experience building and deploying sequence-based deep learning and transformer models
-
Experience extracting, cleaning and working with large structured and unstructured datasets.
-
Exposure to computer vision, or to C#, Java or C/C++.
If this role sounds of interest to you then apply through the link provided below.
Reperio Human Capital acts as an Employment Agency and an Employment Business.
Interview prep pack
Grounded in this listing. Use it to prepare examples before you apply.
Your interview focus
Based on this listing, the role is a hands-on Machine Learning Engineer contract in Dublin, focused on designing, building, deploying, and maintaining NLP and deep learning models for business document processing within an established ML team.
- Hands-on ML and NLP expertise·High
- Production deployment experience·High
- Data preparation and cleaning·Medium
Only have 30 minutes?
Follow a focused preparation plan based on this job.
Start 30-minute prep
Your 30-minute plan
Review and select relevant NLP project examples
0–7 minIdentify 1-2 projects where you built and deployed NLP models, focusing on business document applications.
Refresh knowledge of PyTorch and TensorFlow
7–14 minGo over recent code or tutorials to ensure you can discuss model development and deployment confidently.
Summarize your approach to ML evaluation and monitoring
14–19 minPrepare concise explanations of how you use metrics and monitoring tools to maintain model quality.
Prepare data extraction and cleaning stories
19–25 minRecall specific challenges and solutions from past projects involving large, unstructured datasets.
Draft 2-3 thoughtful questions for the interviewers
25–30 minUse the provided list to tailor questions that show your engagement and understanding of the role.
Talking points
, 5 itemsEnd-to-End ML Solution Delivery
You should be ready to discuss examples where you have designed, built, deployed, and monitored ML or deep learning solutions, as this is a core requirement.
NLP Techniques for Business Documents
Prepare to explain your experience with NLP tasks such as named entity recognition, parsing, classification, clustering, and text prediction, as these are central to the role.
Framework Proficiency (Python, PyTorch, TensorFlow)
Demonstrating hands-on experience with these frameworks will show you can contribute immediately to the team's technical stack.
ML Evaluation Metrics and Model Validation
Be ready to discuss how you use metrics like precision, recall, and approaches to bias/variance to evaluate and improve models.
Data Handling: Extraction, Cleaning, and Preparation
You may be asked about your approach to working with large, structured and unstructured datasets, which is important for NLP and ML tasks.
What to research
, 4 itemsReview recent NLP projects involving business documents
Prepare to discuss specific examples where you applied NLP techniques to extract value from business documents.
Brush up on PyTorch and TensorFlow workflows
Ensure you can speak confidently about building, training, and deploying models using these frameworks.
Revisit ML evaluation metrics and model monitoring strategies
Be ready to explain how you use metrics and monitoring to maintain model performance post-deployment.
Prepare examples of data extraction and cleaning from large datasets
Have stories ready about handling both structured and unstructured data for ML projects.
Questions to ask
, 6 itemsWhat are the main types of business documents the team works with, and what NLP challenges do they present?
Why ask this? Shows your interest in the domain and helps you understand the data landscape.
How is the ML model deployment and monitoring process currently managed?
Why ask this? Clarifies the team's maturity and your potential contributions to production workflows.
What frameworks and tools does the team prefer for NLP and deep learning projects?
Why ask this? Helps you gauge alignment with your technical strengths and identify learning needs.
How often are models retrained, and what triggers a retraining cycle?
Why ask this? Demonstrates your awareness of ML lifecycle management and interest in best practices.
What are the biggest technical or data-related challenges the team is currently facing?
Why ask this? Shows proactive thinking and helps you assess where you can add value.
How does the team collaborate in a hybrid working environment?
Why ask this? Clarifies expectations for communication and teamwork in the hybrid setup.
Register now to upload your CV
Create a free account, save a PDF or Word CV, and quick apply on roles that take applications here.