Senior ML Engineer
As a Senior Machine Learning Engineer, you will be at the forefront of developing cutting-edge AI solutions for enterprises. You will both be implementing existing models, as join the development of custom models. In this position, you will leverage your expertise in machine learning to develop solutions that improve the efficiency or profitability of our customers. You like engaging with customers and colleagues and have a passion for building meaningful solutions using machine learning.
Some key responsibilities:
In your senior role, you assume the position of tech lead on projects, guiding junior colleagues through both technical and client-related aspects. You also play a pivotal role internally, propelling our company's technical prowess and enhancing our offerings to clients
You adeptly comprehend the business needs of customers and convert these into comprehensive technical solutions
Utilize Python to implement production-ready pipelines and machine learning models in the Cloud
Investigate the latest in machine learning research and lead the internal assessment and integration of innovative tools and frameworks
Ensure the high-standard completion of projects managed by your team
Provide technical mentorship to junior machine learning engineers
Conduct workshops for clients, showcasing your knowledge and skills
Apply your expertise to assist the sales team in qualifying and securing exciting new projects
Excellent written and verbal communication skills in native Dutch and English (French is a plus).
Master's or PhD in Computer Science, Artificial Intelligence, or other quantitative field
Minimum of 5 years of experience in machine learning
Proficiency in programming languages such as Python, Java, or Scala.
Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch).
Proven track record of developing and deploying scalable machine learning models.
Excellent problem-solving and analytical skills.
Experience with cloud computing platforms (Microsoft Azure is a plus)
Following experiences are a plus:
Publications in relevant AI/ML conferences and journals is a plus
Experience in mentoring and leading technical teams
Strong communication and teamwork abilities
Experience in one of our focus domains: GenAI, NLP, Vision, Time Series Data, MLOps, Digital Twins, Manufacturing, Finance
We offer:
A rewarding salary package that includes additional perks like a company car and fuel card or a mobility budget, comprehensive hospitalization and group insurance, along with a top-tier laptop and smartphone.
Benefit from a company culture that stimulates both individual and team development, fostering your professional growth.
Utilize your innovation budget for engaging in exciting, educational, and challenging open-source projects within your guild.
Participate in (virtual) team-building activities and gatherings, a great opportunity to unwind and engage with our vibrant team initiatives.
A flexible hybrid working-policy to choose where, how, and when you want to work.
At Faktion we build AI, and we use it thoughtfully in our own processes too. During your application, AI tools may help us with practical steps, like checking that your documents came through complete. They never decide who moves forward: every application is read by a real person on our team, and every decision is made by a human. Curious how this works, or how your data is handled? Just ask, we're happy to explain.
Skills
As published by recruitee · 33 questions · 1 written answer
Basics
Full name, Email, CV, Cover letter, Phone, Photo
Pick from a list (32)
- How would you rate your hands-on skills for Python?
- How would you rate your hands-on skills with FastAPI?
- How would you rate your hands-on skills for Pydantic?
- How would you rate your hands-on skills with Sklearn
- How would you rate your hands-on skills for Pytorch?
- How would you rate your hands-on skills for time series (forecasting / anomaly detection)?
- How would you rate your hands-on skills for binary/multi-label classification models on tabular data (e.g. churn prediction, fraud detection, medical diagnostics,...)?
- How would you rate your hands-on skills for unsupervised learning (clustering, dimensionality reduction, anomaly detection, ...)?
- How would you rate your hands-on skills for recommender systems?
- How would you rate your hands-on skills for OpenCV
- How would you rate your hands-on skills for vision based quality control / anomaly detection?
- How would you rate your hands-on skills with object detection?
- How would you rate your hands-on skills for image segmentation?
- How would you rate your hands-on skills with text representation and embeddings?
- How would you rate your hands-on skills for unsupervised topic modeling?
- How would you rate your hands-on skills for RAG architectures & vector indexing?
- How would you rate your hands-on skills for multi-agent architectures?
- How would you rate your hands-on skills for GenAI evaluation (evals, LLM-as-a-judge, test dataset curation, ...)?
- How would you rate your hands-on skills for langchain?
- How would you rate your hands-on skills for langgraph?
- Have you created custom skills for your agents?
- How would you rate your hands-on skills for AI-assisted coding tools like Cursor, Claude Code, Codex, Antigravity, ...?
- How would you rate your hands-on skills with SQL databases (e.g. Postgres)?
- How would you rate your hands-on skills with document databases (e.g. MongoDB)?
- How would you rate your hands-on skills for vector databases (e.g. Pinecone, Weaviate, pgvector, ...)?
- How would you rate your hands-on skills for graph databases (e.g. Neo4J)?
- How would you rate your hands-on skills for Azure?
- How would you rate your hands-on skills for GCP?
- How would you rate your hands-on skills for AWS?
- How would you rate your hands-on skills with infrastructure as code tools (Terraform/Bicep)?
- How would you rate your hands-on skills with Docker?
- How would you rate your hands-on skills for Git?
Written answers (1)
- IF you have created custom skills, what did you make? optional