Machine Learning Engineer
First seen by Alion on Oct 5, 2026.
The role :
You will join our ML team to turn research into working models for radio access networks. The focus is on predictive and learning-based methods for scheduling, beamforming, and interference management. You will build simulators, train and benchmark models, and help move the best ideas into production-grade pipelines. This is a strong fit if you want your research to ship and be measured against real system-level metrics.
What you'll do :
- Build and maintain Python system-level simulators for multi-cell massive-MIMO networks, including traffic, interference, and realistic channel dynamics.
- Develop deep learning models (graph neural networks, GRU/LSTM, Transformers) for time-series and spatio-temporal prediction in wireless systems.
- Integrate ML predictions into optimization pipelines such as coordinated beamforming, and quantify gains in sum rate, fairness, and cell-edge performance.
- Design reproducible experiments, ablation studies, and benchmarking workflows in PyTorch against classical and learned baselines.
- Explore reinforcement learning and Bayesian approaches for adaptive network decision-making.
- Partner with wireless researchers and software engineers to turn prototypes into reliable data and training pipelines.
- Write up results for internal reviews and, where appropriate, external publications.
What we're looking for :
- MSc (or equivalent) in Data Science, Machine Learning, Electrical Engineering, Statistics, or a related field.
- Strong Python and PyTorch skills, plus a solid grounding in deep learning, time-series modelling, and statistics.
- Hands-on experience with sequence models (RNN, LSTM, GRU, Transformers) and ideally graph neural networks.
- Experience with reproducible ML workflows, including Git, Linux, and experiment tracking.
- Working knowledge of SQL and data pipelines (ETL, validation, modelling).
- Curiosity and rigor : you test assumptions, report negative results honestly, and communicate clearly in English.
Nice to have :
- Exposure to wireless communications (5G/6G, MIMO, beamforming, scheduling).
- Experience with reinforcement learning (DQN, PPO, A2C) or Bayesian inference.
- Cloud data platform experience, such as Microsoft Fabric or Azure (DP-700 is a plus).
- A peer-reviewed publication or a strong research-style thesis.
- Familiarity with C++ or MATLAB for performance-critical or legacy code.
Compensation & benefits :
- Salary : [SEK range, confirm with HM], reviewed annually.
- 30 days paid vacation (Swedish standard of 25 plus company days), occupational pension, and parental leave top-up.
- Wellness allowance and a learning budget for conferences and courses.
- Relocation support and help with work permit or EU Blue Card applications where needed.
- Collective agreement coverage [confirm].
Skills
Machine Learning, Python, PyTorch, Deep Learning, Telecom, Time Series Forecasting