Were looking for a highly skilled, independent, and driven Machine Learning Engineer to lead the design and development of our next-generation real-time inference services - the core engine powering algorithmic decision-making at scale. This is a rare opportunity to own the system at the heart of our product, serving billions of daily requests across mobile apps, with tight latency and performance constraints.
we are a mobile marketing and audience platform. we empower the mobile app ecosystem, simplifying mobile marketing, audience building, and mobile monetization. With direct integration into over 500,000 mobile apps, our platform processes enormous volumes of first-party data to drive intelligent, real-time decisions that fuel growth for our partners.
Youll work at the intersection of machine learning, large-scale backend engineering, and business logic, building robust services that blend predictive models with dynamic, engineering logic - all while maintaining extreme performance and reliability requirements.
Job Description:
Own and lead the design and development of low-latency Algo inference services handling billions of requests per day
Build and scale robust real-time decision-making engines, integrating ML models with business logic under strict SLAs
Collaborate closely with DS to deploy models seamlessly and reliably in production
Design systems for model versioning, shadowing, and A/B testing at runtime
Ensure high availability, scalability, and observability of production systems
Continuously optimize latency, throughput, and cost-efficiency using modern tooling and techniques
Work independently while interfacing with cross-functional stakeholders from Algo, Infra, Product, Engineering, BA & Business.
Requirements: B.Sc. or M.Sc. in Computer Science, Software Engineering, or a related technical discipline
5+ years of experience building high-performance backend or ML inference systems
Deep expertise in Python and experience with low-latency APIs and real-time serving frameworks (e.g., FastAPI, Triton Inference Server, TorchServe, BentoML)
Experience with scalable service architecture, message queues (Kafka, Pub/Sub), and async processing
Strong understanding of model deployment practices, online/offline feature parity, and real-time monitoring
Experience in cloud environments (AWS, GCP, or OCI) and container orchestration (Kubernetes)
Experience working with in-memory and NoSQL databases (e.g. Aerospike, Redis, Bigtable) to support ultra-fast data access in production-grade ML services
Familiarity with observability stacks (Prometheus, Grafana, OpenTelemetry) and best practices for alerting and dignostics
A strong sense of ownership and the ability to drive solutions end-to-end
Passion for performance, clean architecture, and impactful systems
This position is open to all candidates.