Junior Machine Learning Engineer - Internship

2w2 weeks ago

Visium

Lausanne, CH · Internship · CHF 48,000 – CHF 72,000

About this role

Join Visium's growing team and take part in expansion in Switzerland and internationally. As a Junior Machine Learning Engineer, work on a variety of applied research projects using state-of-the-art Machine Learning techniques. Help deploy them for customers, transforming their business.

Contribute to an exciting environment with a challenging team always on the lookout to learn and share about the latest technologies and applications. Dive into data enthusiastically, asking the right questions on non-textbook projects. Bring imaginative solutions with a problem-solving attitude.

At Visium, enable enterprise executives in defining AI & Data strategy and executing large-scale transformations. Empower clients to unleash and scale the power of their data with expertise in strategy, architecture, cloud engineering, analytics, AI and ML. Pioneer a bright future and build future-proof ethical organizations.

Join the curious, ambitious doers and good-hearted Visiumees who build a world we're all in awe of. Gain a position enabling impact on thousands of people and the company's growth. Work with an international, knowledgeable, passionate team with strong collaborative mindset.

Requirements

EPFL or ETH Student/Graduate; if studying, internship to complete as part of studies
Proficiency in Python programming
Experience with ML/DL libraries and frameworks (Scikit-learn, TensorFlow, Keras, PyTorch)
Experience with visualization frameworks (Matplotlib, Seaborn, Bokeh, Power BI, Tableau, D3.js)
Experience applying deep learning approaches like RNNs and CNNs
Strong knowledge of clustering algorithms, regression and classification (supervised/unsupervised/reinforcement learning)
Understanding of Unix/Linux operating systems
Strong communication skills to present to non-technical people

Responsibilities

Work on a variety of applied research projects using state-of-the-art Machine Learning techniques
Help deploy ML solutions for customers, transforming their business
Implement ML/DL models using Scikit-learn, TensorFlow, Keras, PyTorch
Apply deep learning approaches such as recurrent neural networks and deep convolution networks
Utilize clustering algorithms, regression and classification in supervised, unsupervised, reinforcement learning
Create visualizations with Matplotlib, Seaborn, Bokeh, Power BI, Tableau or D3.js
Present results to non-technical stakeholders
Develop familiarity with REST API and microservices for deployment