{"id":1904930,"url":"https://alion.io/job/versatile-machine-learning-engineer","title":"Machine Learning Engineer","company":{"id":2950784,"name":"Versatile","domain":"versatile.club","url":"https://alion.io/company/versatile-club","size_band":"11-50","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["India"],"countries":["IN"],"hiring_countries":["IN"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":35000,"max_usd":79000,"period":"year","method":"role_seniority_country_cell","sample_n":13},"experience_years_min":11,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"ETL/ELT","optional":false},{"name":"Git","optional":false},{"name":"Linux","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"PyTorch C++","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"SQL","optional":false},{"name":"Time Series Forecasting","optional":false},{"name":"Transformers","optional":false},{"name":"Azure","optional":true},{"name":"C++","optional":true},{"name":"MATLAB","optional":true},{"name":"Microsoft Fabric","optional":true},{"name":"PPO","optional":true}],"status":"live","first_seen_at":"2026-10-05T11:22:42Z","employer_posted_date":null,"last_verified_at":"2026-10-05T11:22:42Z","board_verified":false,"closed_at":null,"days_open":3,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":3},"description":"The role : \n\nYou 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.\n\nWhat you'll do : \n\n- Build and maintain Python system-level simulators for multi-cell massive-MIMO networks, including traffic, interference, and realistic channel dynamics.\n\n- Develop deep learning models (graph neural networks, GRU/LSTM, Transformers) for time-series and spatio-temporal prediction in wireless systems.\n\n- Integrate ML predictions into optimization pipelines such as coordinated beamforming, and quantify gains in sum rate, fairness, and cell-edge performance.\n\n- Design reproducible experiments, ablation studies, and benchmarking workflows in PyTorch against classical and learned baselines.\n\n- Explore reinforcement learning and Bayesian approaches for adaptive network decision-making.\n\n- Partner with wireless researchers and software engineers to turn prototypes into reliable data and training pipelines.\n\n- Write up results for internal reviews and, where appropriate, external publications.\n\nWhat we're looking for : \n\n- MSc (or equivalent) in Data Science, Machine Learning, Electrical Engineering, Statistics, or a related field.\n\n- Strong Python and PyTorch skills, plus a solid grounding in deep learning, time-series modelling, and statistics.\n\n- Hands-on experience with sequence models (RNN, LSTM, GRU, Transformers) and ideally graph neural networks.\n\n- Experience with reproducible ML workflows, including Git, Linux, and experiment tracking.\n\n- Working knowledge of SQL and data pipelines (ETL, validation, modelling).\n\n- Curiosity and rigor : you test assumptions, report negative results honestly, and communicate clearly in English.\n\nNice to have : \n\n- Exposure to wireless communications (5G/6G, MIMO, beamforming, scheduling).\n\n- Experience with reinforcement learning (DQN, PPO, A2C) or Bayesian inference.\n\n- Cloud data platform experience, such as Microsoft Fabric or Azure (DP-700 is a plus).\n\n- A peer-reviewed publication or a strong research-style thesis.\n\n- Familiarity with C++ or MATLAB for performance-critical or legacy code.\n\nCompensation & benefits : \n\n- Salary : [SEK range, confirm with HM], reviewed annually.\n\n- 30 days paid vacation (Swedish standard of 25 plus company days), occupational pension, and parental leave top-up.\n\n- Wellness allowance and a learning budget for conferences and courses.\n\n- Relocation support and help with work permit or EU Blue Card applications where needed.\n\n- Collective agreement coverage [confirm].\nSkills\nMachine Learning, Python, PyTorch, Deep Learning, Telecom, Time Series Forecasting","description_format":"text","description_chars":2924,"description_truncated":false,"requirements":{"experience_years_min":11,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Parental 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