Viktor Cikojević, PhD

Senior ML research engineer with end-to-end ownership across generative modelling, LLM systems and production deployment. At Salient Predictions (acquired by a top-3 hyperscaler) I owned the generative-modelling line across all three generations of the GEM family: I designed and trained the diffusion and flow-matching architectures behind GEM-1, then co-led GEM-2 (arXiv:2601.03753): a ~275M-parameter one-shot extension of diffusion models built on functional generative networks (FGN), cutting training cost ~100× and inference cost ~10× against multi-step diffusion ensembles such as GenCast, and co-led GEM-3 (arXiv:2608.06241), Salient's latest production model. Alongside that I have shipped a containerised audio-transcription SaaS on GCP and RunPod, and have hands-on LLM fine-tuning, RAG and multi-agent experience from Kaggle competitions and side projects. Published researcher (2 US AI patents, 10 Q1 physics papers, ~300 citations) and Kaggle Expert, top 551 of 205,000. PhD in computational physics, with deep PyTorch and distributed multi-GPU training experience.

1. Machine Learning Research Engineer

   Dec 2023 – Feb 2026

   Salient Predictions (acquired by a top-3 hyperscaler) · Split, Croatia (remote)

   • Owned Salient's generative-modelling line end to end across all three generations of the GEM family (GEM-1, GEM-2, GEM-3): architecture design, training-data preparation, distributed multi-GPU training, evaluation systems (CRPS, REV, calibration, loss-pattern diagnostics), and production deployment. GEM set the state of the art in global probabilistic weather forecasting.
   • Co-led the technical design and trained the diffusion and flow-matching generative models powering GEM-1: high-dimensional conditional sampling over global atmospheric state, with controllable, calibrated ensembles.
   • Co-led the technical design and training of GEM-2 (equal contribution, arXiv:2601.03753): a ~275M-parameter one-shot extension of diffusion models via functional generative networks (FGN), jointly modelling global atmospheric dynamics and decision-centric variables (daily Tmax/Tmin, precipitation, wind extremes); outperforms NOAA GEFS and IFS ENS 46 on CRPS skill at 1–40 day leads; state-of-the-art relative economic value (REV); positive skill maintained to 126 days (S2S range).
   • Co-led and co-author on GEM-3 (arXiv:2608.06241), Salient's latest production generative model.
   • Cut training cost ~100× and inference cost ~10× versus multi-step diffusion ensembles (e.g. GenCast) by collapsing iterative denoising into single-shot generation, while preserving ensemble calibration, fidelity and diversity.
   • Co-invented 2 US AI patents, including GEM-2.
   • Delivered the prior-generation production model: +20% skill score over the previous baseline.

2. Data Scientist

   Jan 2022 – Dec 2023

   Bellabeat (Y Combinator backed) · Split, Croatia (remote)

   • Developed a multi-task transformer (encoder-decoder) for menstrual health prediction: period start MAE 2.3 days, period end MAE 0.68 days (~50% improvement over baseline), ovulation F1 0.922; published article.
   • Deployed PyTorch models on-device for the iOS and Android apps, converting to Core ML (iOS) and ONNX (Android) for low-latency, offline inference.
   • Shipped user-facing health features end to end, from data engineering and production pipelines, monitoring and governance on GCP through to report design, working with product and design to put model outputs in front of users.
   • Owned product and business analytics: instrumented app usage, built onboarding and subscription funnels, retention and engagement cohorts, and experiment readouts that shaped product and growth decisions.

3. Computer Vision Engineer

   Jul 2021 – Jan 2022

   Codeasy / Necogi · Split, Croatia

   • Built an aerial imagery analysis pipeline in PyTorch for object detection and classification.

4. PhD in Computational Quantum Physics (with honours)

   Dec 2016 – Oct 2021

   UPC Barcelona & University of Zagreb

   • Developed high-performance Quantum Monte Carlo simulation software in C++ for probabilistic sampling.
   • Built Python pipelines for large-scale data analysis; published 10 Q1 papers with ~300 citations.
   • PhD thesis · Google Scholar

5. MSc in Computational Physics — CGPA 5.0/5.0

   Sep 2014 – Sep 2016

   University of Split · Split, Croatia

   • Coursework in computational simulations; master's thesis on quantum Monte Carlo, with an Erasmus exchange at UPC Barcelona.

Machine learning. P. Rauba*, V. Cikojević*, F. Bartolić*, S. Levang* et al., “Probabilistic Transformers for Joint Modeling of Global Weather Dynamics and Decision-Centric Variables,” arXiv:2601.03753, 2026 (*equal contribution). Read on arXiv.

Machine learning. “Timestep-Conditioned Transformers for Global Weather Forecasting” (GEM-3), arXiv:2608.06241, 2026 (co-author). Read on arXiv.

Physics. 10 peer-reviewed publications in Physical Review A/B/Research and New Journal of Physics, ~300 citations. Google Scholar.

• RSNA 2024 — Lumbar Spine Degenerative Classification. Medical imaging (MRI). 15th of 1,874, top 1% — solution writeup.
• HMS — Harmful Brain Activity Classification. EEG-based seizure detection. 54th of 2,767, top 2%.
• Detect Sleep States. Time-series accelerometer data. 42nd of 1,877, top 2%.
• Further results. Vesuvius Challenge (computer vision / 3D segmentation, 39th of 1,249, top 3%), LLM 20 Questions (LLM agents, 87th of 832), SenNet blood-vessel segmentation (3D medical imaging, 137th of 1,149, top 12%), ISIC 2024 (computer vision / medical imaging, 243rd of 2,739), LLM Science Exam (RAG + fine-tuned LLMs under GPU/time limits, 678th of 2,664).

• transcribevoice.app & transcribe-api-endpoint — production audio-transcription SaaS. Containerised WhisperX + NeMo (speaker diarisation) inference service with a multi-stage Docker image, deployed on RunPod serverless. Next.js frontend on Vercel, Firebase Auth/Firestore, Google Cloud Storage for audio and transcripts, Stripe Checkout for credits, GCP Cloud Functions for the upload and transcoding pipeline (yt-dlp + ffmpeg).
• Kaggle LLM 20 Questions — multi-agent self-play. Questioner/answerer agent pair using LangChain with a Chroma vector store, and DSPy; ColBERT for retrieval; local inference via llama-cpp-python and Ollama; self-play orchestrated across category templates.
• Numerai — classical ML in production. CatBoost/XGBoost regression on encrypted, era-structured financial features with multi-target training; era-aware hyperparameter sweeps and OOF evaluation; daily inference deployed on GCP Cloud Run behind an HTTP trigger.

Languages

Python, C/C++, SQL, Bash

Computer vision

Object detection and classification, semantic and 3D/volumetric segmentation (U-Net, segmentation_models_pytorch), keypoint detection and heatmap regression, geometric registration and 3D coordinate transforms (DICOM patient-space), tiled sliding-window and chunked inference over gigapixel/volumetric data, test-time augmentation, ensembling, timm backbones (EfficientNet, ConvNeXt, NFNet, SEResNeXt), albumentations, OpenCV, ONNX Runtime / Core ML deployment

Generative modelling

Diffusion models, flow matching, functional generative networks (FGN), one-shot generation, probabilistic transformers, conditional sampling, ensemble calibration

ML / deep learning

PyTorch, PyTorch Lightning, distributed multi-GPU training, transformers, TensorFlow, Keras, CNNs, LSTMs, NLP

Classical ML

XGBoost, CatBoost, scikit-learn, tree ensembles, feature engineering, hyperparameter optimisation, model evaluation

LLM & agents

LLM fine-tuning (PEFT/LoRA, 8-bit quantisation with bitsandbytes), RAG (sentence-transformers, FAISS, Chroma, ColBERT), LangChain, DSPy, Chainlit, multi-agent self-play, Pydantic-typed outputs, llama-cpp-python, Ollama

Production / MLOps

Docker (multi-stage builds), RunPod serverless, GCP (Cloud Run, Cloud Functions, Cloud Storage, BigQuery), Firebase (Auth, Firestore), Vercel/Next.js integration, FFmpeg media pipelines, Git/GitHub, GitLab

Data tooling

xarray, dask, Pandas, NumPy, WandB, Hydra

Running and swimming, guitar, travelling.

For consulting and project work, see Luna AI Lab or email viktor.cikojevic@luna-ai-lab.com.