Engineering Manager

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.

As an Engineering Manager, you will lead the team behind Truecaller's Recommendations and adVantage, our AI-powered performance advertising platform. It is a multi-disciplinary team of ML engineers, data scientists, backend engineers, and data engineers building the retrieval, ranking, and contextual-bandit systems, and the real-time serving and data infrastructure beneath them, that decides what Truecaller recommends and shows to hundreds of millions of users. The role sits at the intersection of technical depth and people leadership: you will set direction across an applied-ML stack, keep a highly specialized team healthy and growing, and turn a research-heavy roadmap into shipped, reliable systems.

• →Lead, grow, and retain a high-performing team of ML engineers, data scientists, backend engineers, and data engineers, investing deliberately in each person's development and career path.
• →Set technical direction across the recommendation and advertising ML stack - retrieval, ranking, contextual bandits, and conversion modeling - along with the real-time serving and data platforms that support them.
• →Own team planning, prioritization, and delivery, balancing near-term product and campaign commitments against long-term platform health.
• →Partner closely with product, data science, and other engineering teams to translate business goals - revenue, engagement, advertiser outcomes - into a clear technical roadmap.
• →Push for reuse: config-driven, composable platforms and closed feedback loops rather than one-off models and pipelines.
• →Drive rigor in experimentation and evaluation, from offline metrics to online A/B tests, so model changes ship on measured impact.
• →Keep production systems reliable at scale, owning the latency, quality, and cost of models serving hundreds of millions of users.
• →Run regular 1:1s, performance reviews, and career development conversations, and recruit and onboard new talent as the team grows.
• →Stay close enough to the technical detail to make informed calls, unblock your team, and earn credibility with senior ML and backend engineers.

• Proven experience leading or managing engineering or ML teams, ideally in recommendations, ranking, ads, search, or a similarly ML-heavy domain.
• A solid grasp of applied ML in production - retrieval and ranking, experimentation, model serving, and the data that feeds them - enough to engage credibly on technical trade-offs. You do not need to be the strongest modeler in the room; you need to lead the people who are.
• Working knowledge of backend and data engineering practices - architecture, real-time systems, pipelines, and warehousing - enough to reason about the full stack your team owns.
• Strong leadership and communication skills, with the ability to align specialists with different skill sets toward shared goals.
• A track record of delivering complex technical projects on time and at scale.
• Comfort operating in ambiguity and making pragmatic trade-offs between speed and quality.
• A collaborative mindset and the ability to work closely with cross-functional stakeholders.

• A hands-on background in ML engineering, data science, or backend development before moving into management.
• Experience with recommender systems, contextual bandits or reinforcement learning, or performance advertising and ad-serving systems.
• Familiarity with large-scale data and ML platforms (Spark, Kafka, Airflow, feature stores, model serving).
• Familiarity with cloud infrastructure (GCP preferred; AWS or Azure also fine).
• Experience scaling teams in a high-growth tech environment.