Build, train, and ship ML models for identity verification use cases such as biometric matching, liveness / anti-spoofing, identity document processing (OCR/extraction), and fraud detection (team assignment based on experience).
Prepare large, noisy datasets: ingestion, validation, cleaning, deduplication, labeling strategy, and dataset QA to improve model performance and reliability.
Design experiments, evaluation protocols, and success metrics (offline and online), iterate based on measurable business impact (detection rates, fraud losses, false positives).
Develop production-grade training and inference pipelines on AWS with strong reproducibility, monitoring, and cost controls.
Productionize models as resilient services and libraries in Python; collaborate with platform teams on APIs, latency and observability.
Contribute to the transformation of our IDV engine: modernizing legacy components, improving modularity, and raising quality, performance, and maintainability.
Work closely with Product, Customer Success, and Platform Engineering teams to ensure ML solutions meet privacy, compliance, and reliability requirements.
Support other engineers through design reviews, code reviews, and knowledge sharing; help raise the technical bar across the team.

Bachelors degree in computer science or related field paired with knowledge, skills and abilities typically gained from 2-5 years of experience in applied machine learning / ML engineering with strong software engineering fundamentals (or equivalent combination of education and experience).
Strong Python skills and experience building production ready code.
Demonstrated experience solving computer vision tasks with ML models utilizing PyTorch or Tensorflow.
Strong computer vision background, including experience with CNNs, vision transformers, and foundation models.
Proven ability to work with large datasets and build reliable data preprocessing/feature engineering pipelines; comfort with distributed data tooling is a plus.
Clear communication skills and the ability to work effectively across engineering, product, and operations stakeholders.

Wellness: Universal, supplemental, and private healthcare plan choices based on country specifics
Financial future: retirement/pension plan contributions, MTK stock plan participation
Income protection: life event & disability coverage
Paid time off: generous annual leave, company holidays, volunteer time off
Learning: e-learning license, tuition reimbursement, hackathons
Home office setup allowance
Additional/optional benefits: pet insurance, identity theft protection, legal assistance

Machine Learning Engineer

Key skills