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Senior Machine Learning Engineer at Acast
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Uppsala University
Master of Science (M.Sc.), Computer and Information Engineering
January 1, 2011 – January 1, 2016
Acast
Senior Machine Learning Engineer
December 1, 2025 – Present
Acast
Senior Data Scientist / Engineer
April 1, 2020 – December 1, 2025
Acast
Data Scientist / Machine Learning Engineer
October 1, 2019 – March 1, 2020
EQT Group
Data Scientist / Engineer
January 1, 2019 – September 1, 2019
Stockholm, Stockholm County, Sweden · On-site
Netlight
Data Science Consultant
February 1, 2018 – March 1, 2020
Trafikförvaltningen, Region Stockholm
Data Scientist / Engineer
February 1, 2018 – December 1, 2018
Stockholm, Stockholm County, Sweden · On-site
Telia Company
Machine Learning Researcher and Developer
October 1, 2016 – January 1, 2018
Greater Uppsala Metropolitan Area
Telia Company
Deep Learning Researcher
March 1, 2016 – October 1, 2016
Greater Uppsala Metropolitan Area
Convolutional Neural Networks for Natural Language Understanding (Master thesis)
March 1, 2016 – October 1, 2016
• Implemented Convolutional Neural Networks in TensorFlow for semantic classification of transcribed utterances . • Scikit-learn was used to establish baseline performances of Support Vector Machines and Naive Bayes classifiers. • Implemented tools in Python for pre-processing, analyzing, and visualizing data.
Training Neural Networks: Backpropagation vs Particle Swarm Optimization
May 1, 2015 – Present
• Conducted a comparative study on training artificial neural networks with backpropagation with gradient descent and particle swarm optimization across multiple classification tasks. • Experiments were coded in Matlab.
Sequence Models
Coursera
June 24, 2026 – Present
Structuring Machine Learning Projects
Coursera
June 24, 2026 – Present
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Coursera
June 24, 2026 – Present
Neural Networks and Deep Learning
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's experience spans various industries (media, finance, public transport, telecom) and types of organizations (start-up, large enterprise, consulting), demonstrating adaptability and a broad perspective. The progression within Acast from contract to full-time and then to senior roles suggests a commitment to long-term engagement and growth within an organization. The focus on ML/AI across all roles indicates a clear passion and specialization, which aligns well with a dedicated ML Engineer role.
Soft Skills & Operational Fit
The candidate's career progression from Data Scientist to Senior Machine Learning Engineer at Acast indicates strong growth potential and ability to take on increasing responsibilities. Experience in consulting roles (Netlight) and contract work suggests adaptability and project-based problem-solving skills. The descriptions of developing AI tools for venture capitalists and deep learning models for train infrastructure suggest an ability to apply ML to diverse real-world problems, indicating strong operational fit.