Join Truecaller – The place where innovation meets impact!
Truecaller's mission is to build trust in communication by making it safer, smarter, and more efficient. Born in Sweden, trusted by the world, and here’s why we stand out:
- We are trusted by over 500 million active users every month across 190+ countries
- We identify over 15 billion calls daily, helping users avoid spam and scams
- We are powered by a team of 400+ employees from 45+ nationalities
We always look for people who take initiative, own their work, and keep raising the bar. An entrepreneurial mindset matters here, especially when it turns bold ideas into real actions. We stay collaborative and focused, always searching for smarter paths forward. If you want to make an impact and grow with a team that inspires millions, you’ll fit right in.
The role:
As a Senior ML Engineer, you'll play a central role in building the data science behind the products — from framing new fraud, risk, and intelligence problems, to designing and deploying ML models at scale, to helping our enterprise customers and go-to-market teams understand and act on the insights we generate.
What you’ll do:
- Design, build, and continuously improve the ML models that power our risk and intelligence products, and take ownership of new signals as they get scoped.
- Take a loosely defined business or customer problem and break it into a clear data problem, articulating value, impact, and complexity before proposing a solution.
- Build anomaly detection, fraud, and risk-modeling approaches — including network/graph-based methods — that keep our signals accurate and resistant to adversarial behaviour.
- Own model development, deployment, and monitoring end-to-end, partnering with ML/data engineers on scalability, reliability, cost, and dashboards/alerting.
- Design and run experiments (A/B tests, offline customer POCs) to validate new signals before they roll into production.
- Manage and analyse large, multi-country datasets, ensuring data integrity, consistency, and compliance throughout.
- Partner cross-functionally with Product, Engineering, Legal, and GTM/Sales to scope, prioritise, and ship data products on time, acting as a trusted advisor on what the data can and can't responsibly support.
What you bring in:
- 5+ years of experience designing, building, and deploying ML models at scale, ideally including risk/fraud, propensity, or behavioural/network scoring use cases.
- Strong grounding in applied machine learning: classification, anomaly detection, propensity/scoring models, clustering, and time-series/drift monitoring.
- Exposure to graph-based analysis or graph ML (network embeddings, community detection, link prediction) is a plus.
- Hands-on experience taking models from research/experimentation into production — comfortable owning scalability, reliability, and monitoring, not just model accuracy.
- Working knowledge of NLP and LLM-based techniques (prompting, summarisation, fine-tuning) — useful for customer-facing AI insights and on-device text/SMS signal extraction.
- Proficiency in Python and the ML/data stack: Pandas, NumPy, Scikit-learn, TensorFlow or PyTorch; comfortable with Hugging Face Transformers where relevant.
- Strong SQL skills and experience with large-scale data processing (BigQuery, Spark/PySpark, Hive/Kafka ecosystem).
- Familiarity with database modelling and data warehousing principles.
- Ability to design, run, and interpret experiments and statistical tests to validate model and business impact.
- Strong communication skills — able to explain model output and trade-offs to both engineering peers and non-technical enterprise stakeholders.
- Comfort op