Senior ML Engineer

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 450 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 450+ 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:

In the Search Team, we are responsible for Spam Detection, AI Identity, Name Resolution, and Fraud Detection. By accurately assigning names to phone numbers, detecting spam and fraud, and providing contextual caller information, we enhance users' trust and safety, empowering them to make informed decisions about incoming calls and directly supporting Truecaller’s mission of enabling safe and trusted communication.

As a Senior Machine Learning Engineer in the Search team, you will play a key role in building and scaling data pipelines, frameworks, and ML models that help us better understand our users and make smarter product decisions. You’ll work hands-on to design robust, scalable solutions that power a self-serve analytics platform for product teams, enabling faster insights and data-driven development across the company.

In this role, you’ll also take ownership of optimising the training and deployment of machine learning models to be fast, reliable, and cost-efficient. You will be at the forefront of improving data privacy and customer experience. This includes collaborating closely with data scientists, analysts, and mobile/product teams to establish best practices, tooling, and workflows for efficient ML development and deployment.

You’ll be expected to help push the team to the cutting edge of ML training and deployment.

What you’ll do:

• Understand current and future needs of developers and the organisation, and define a roadmap with your manager
• Enable teams across Truecaller to deploy ML models and data pipelines quickly and cost-effectively
• Optimise ML models and act as the go-to expert for model development and deployment
• Maintain and improve high-throughput data pipelines, delivering billions of events reliably within SLAs
• Support teams in building and optimising complex pipelines and ML workflows
• Collaborate with data scientists, analysts, and other teams to bring models and insights into production
• Develop tools and frameworks to improve the data and ML platform
• Work on large-scale initiatives, including ML platforms and streaming use cases
• Establish best practices for software, data, and ML development
• Stay up to date with ML trends and drive continuous innovation

What you bring in:  

• Degree in computer science or equivalent
• 5+ years of experience in machine learning engineering, with a focus on deploying ML models in production environments.
• A strong understanding and experience to optimise feature engineering, model training, and model deployment.
• Hands-on experience deploying ML models.
• Strong understanding of machine learning frameworks (TensorFlow, PyTorch, ONNX) and their deployment.
• Understanding of both Android and iOS development and how that impacts ML model deployment and usage.
• Experience working with orchestration tools (ex, Airflow).
• Experience working in a big data environment (ex, Hadoop).
• Experience building complex ETL pipelines.
• Experience working with cloud providers such as GCP, AWS, or Azure
• Strong programming skills in Spark with Scala and Python
• Deep understanding of internal of Spark with experience in optimizing Spark jobs
• Strong understanding of Software Engineering practices and principles.
• Have worked on building and