Machine Learning Engineer | Syndata AB · Teeming.ai
Syndata AB
Accelerate your Business with synthetic data
Through the use of Machine Learning, Artificial Intelligence and custom algorithms, Syndapp can generate large data sets that match the statistical…
Accelerate your Business with synthetic data
Through the use of Machine Learning, Artificial Intelligence and custom algorithms, Syndapp can generate large data sets that match the statistical…
Product: Synapp — generative-AI synthetic data platform (SaaS)
Use cases: Privacy-preserving data sharing, testing, machine learning
Founder: Hannes Sapiens Sjöblad
Employees (reported): 2
Known funding: SEK 2.2M seed (2021) reported by Dealroom
Company Overview
Problem Domain
Synthetic data generation for privacy, data sharing, testing, and machine learning—targeting regulated sectors such as healthcare and finance.
Industry
Synthetic data / Generative AI
Funding Track Record
Seed- 2021
SEK 2.2 million
Amount and year reported by Dealroom; other aggregator profiles list funding rounds but with obfuscated details.
Founders
What we do
Join the Team
Machine Learning Engineer
HybridStockholm, Stockholm County, SE
Hybrid • Stockholm, Stockholm County, SE
Syndata
is seeking a
Data Scientist / Machine Learning Engineer
to join our research and engineering team working on
LeakPro 2.0
– our next-generation framework for evaluating privacy leakage, synthetic data utility, and AI robustness.
The role can be
part-time (minimum 50%) or full-time
, and applicants are encouraged to specify their preferred workload.
This is an opportunity to contribute to cutting-edge work at the intersection of
synthetic data
,
generative AI
,
privacy-preserving technologies
, and
machine learning engineering
.
Key Responsibilities
Software & Engineering Skills
Advanced Python programming skills.
Experience with PyTorch or TensorFlow.
Ability to write clean, modular, well-documented research code.
Familiarity with benchmarking frameworks, automated testing and reproducible ML pipelines.
Strong version control discipline (Git).
Collaboration & Communication
Ability to explain complex technical concepts to non-expert audiences.
Experience working in cross-disciplinary research settings.
Comfortable interfacing with legal and compliance topics related to AI.
Who We’re Looking For
A candidate with a strong analytical mindset, interest in rigorous experimentation, and the ability to move between theoretical reasoning and practical engineering. You enjoy building high-quality research tools, thinking deeply about privacy and risk, and contributing to a product with real societal impact.
Employment Details
Workload:
50–100% (state your preference in the application)
Location:
Remote-first and/or Stockholm optional
Start date:
As soon as possible
Contract:
Project-based employment
Compensation:
Competitive, based on experience
For more information or questions please contact us at
mattias@syndata.co or phone number +46700079460
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Design and implement empirical evaluation pipelines for privacy leakage, robustness and utility testing.
Develop, train and benchmark generative models (GANs, VAEs, diffusion models, LLM-based generators) across multiple data modalities (tabular, image, text, time-series).
Investigate and execute privacy attack vectors such as membership inference, singling out, attribute inference and linkability.
Contribute to defining and improving the scientific foundations behind LeakPro 2.0.
Build modular, reproducible ML systems and research code using modern engineering practices.
Collaborate with legal and policy teams to translate technical results into insights relevant for GDPR, the AI Act and the EHDS.
Required Technical Qualifications
Strong statistical grounding and experience with empirical research or experiment design.
Practical experience generating synthetic data in one or more modalities (tabular, image, text, time-series).
Proficiency with modern generative models: GANs, VAEs, diffusion models, or LLM-based synthesizers.
Knowledge of privacy-preserving methods, including:
Differential Privacy (e.g., DP-SGD)
Federated Learning
Secure Multi-Party Computation
Zero-Knowledge Proofs (nice to have)
Understanding of privacy risks and attack techniques.
Experience training and evaluating machine learning models.
