Data Scientist Biomedical Signal Processing & ML

Náplň práce, právomoci a zodpovednosti:

Seerlinq is a medical telemonitoring platform analyzing PPG and ECG biosignals from wearable devices. This role sits in the R&D team and is focused on researching, developing, and productionizing clinical algorithms. Current algorithms cover heart failure risk indices, AFib detection, heart rate, respiratory rate, SpO2, and signal quality. Algorithms run as production workers on AWS EKS. Experiments are tracked with MLflow and data is stored on AWS S3.

Responsibilities

• Develop new clinical detection algorithms — next priorities are focued on new cardiovascular conditions and ECG feature extraction

• Maintain and benchmark existing algorithms (AFib, heart failure, signal quality)

• Full ML lifecycle: preprocessing → feature engineering → model training → evaluation → production packaging

• Work with real clinical datasets (MIMIC, Happitech, SmartCare, CapnoBase) and internal labelled data

• Contribute to LLM fine-tuning: evaluating fine-tuned medical models on our proprietary data (MedGemma vs Gemma baseline)

• Write tests (pytest), docstrings, and maintain reproducible experiments

Why join Seerlinq?

• Mission-driven, experienced collegues with strong academic and technical backgrounds

• Secured grant to expand globally

• Opportunity to regularly publish in Q1/Q2 medical journals

Zamestnanecké výhody, benefity:

• A dynamic role in a future-oriented company

• A young and friendly team

• A modern and comfortable work environment

• Continuous opportunities for professional and personal growth

• Care for your health 

• Company events and team-building activities

Informácie o výberovom konaní:
If you're interested, feel free to contact us by responding to the advertisement or via email at: careers@seerlinq.com

Požiadavky na zamestnanca:

Tech Stack

Python 3.13, scikit-learn, LightGBM, XGBoost, PyTorch, MLflow, scipy, xarray, AWS S3

Required

• Strong Python for data science (numpy/scipy/pandas/sklearn stack)

• Signal processing fundamentals (filtering, beat detection, feature extraction)

• Classical ML + at least one DL framework

• Git, reproducible experiment discipline

Nice to Have

• Biomedical or clinical background

• ECG/PPG domain knowledge

• LLM fine-tuning experience (PEFT, QLoRA)

• MLflow or similar experiment tracking

• Timeseries foundational models