Machine Learning Developer; EP-CMG-OS--GRAP

Employment Type:

Full-time

Join us to push the boundaries of real-time Machine Learning (ML) in one of the most demanding computing environments in the world. You will develop cutting‑edge ML models for the CMS Level‑1 Trigger – an ultra‑low‑latency, FPGA‑based system responsible for selecting the most interesting LHC collisions in real‑time.

You will help design the next generation of trigger algorithms for the High Luminosity LHC era by co‑training ML models across different systems to maximise physics performance while optimising information flow, bandwidth, and on‑device resource usage. This includes developing and scaling MLOps workflows, integrating ML models into FPGAs, and delivering demonstrators that validate full‑chain performance from training and physics performance to on‑hardware deployment.

This position is part of the Next Gen Triggers (NGT) project, a 5‑year collaboration between LHC experiments and the CERN Research and Computing Departments. The project leverages innovative Artificial Intelligence technologies and high‑performance computing architectures to enhance trigger selection, data processing, and theoretical interpretation for LHC experiments. The insights gained will inform future detector development, data flows, and theoretical tools.

• Design and train ML models to boost the physics selections of the CMS Phase‑2 Level‑1 Trigger by targeting specific signatures and optimising information transport across the multi‑algorithm system.
• Develop, deliver, integrate and test ML models in FPGAs (including RTL/HLS components and software emulators).
• Demonstrate physics performance gains and present results within CMS, at CERN, and at international conferences.
• Design and incorporate MLOps practises, scaling up workflows to ensure reproducible training, validation and deployment of ML‑based trigger algorithms.
• Collaborate closely with colleagues in CMS, CERN departments, and external institutes working on ML‑for‑Trigger research.

Experience:

• Experience developing and applying Machine Learning algorithms for physics or scientific data analysis;
• Familiarity with Fast ML / hardware‑constrained ML techniques is an advantage;
• Knowledge of physics analysis or physics event reconstruction methods;
• Experience with Trigger and Data Acquisition systems, including hardware architectures;
• Practical experience with software development (e.g. Git Hub/Git Lab, Continuous Integration, MLOps);
• Basic knowledge of FPGA design including HDLs (VHDL/Verilog) and/or High Level Synthesis (C++);

• Machine Learning & Fast Machine Learning;
• Physics Data Analysis & Reconstruction;
• Trigger Systems & Data Acquisition (TDAQ);
• MLOps, Continuous Integration (CI) & CI/CD Pipelines;
• FPGA Design & Programming;
• Hardware Description Languages (HDL) & High‑Level Synthesis (HLS);
• Spoken and written English, with a commitment to learn French.

• You are a national of a CERN Member or Associate Member State.
• You have a professional background in Physics (or a related field) and have either:
  • a Master's degree with 2 to 6 years of post‑graduation professional experience;
  • or a PhD with no more than 3 years of post‑graduation professional experience.
• You have never had a CERN fellow or graduate contract before.

Job closing date: 04.02.2026 at 23:59 CET.

Contract duration: 24 months, with a possible extension up to 36 months maximum.

Working hours:

40 hours per week

Job flexibility:
Hybrid

Target start date: 01-April-2026

Job reference: EP-CMG-OS--GRAP

Field of work:
Applied Physics

Benchmark job: 200140 - Applied Physicist

• A monthly stipend between  Swiss Francs per month (tax free) depending on your degree.
• 30 days of paid leave per year plus 2 weeks annual closure.
• Coverage by CERN’s comprehensive health insurance scheme (for yourself, your spouse and children), and membership of the CERN Pension Fund.
• Family, child and infant monthly allowances depending on your individual circumstances.
• A relocation package (installation grant and travel expenses) depending on your individual circumstances.
• Possibility to extend your contract up to 36 months.
• On-the-job and formal…