You will work at the intersection of backend engineering and ML infrastructure - building recommendation engines, reranking services, user profile systems, and model serving pipelines. This role requires engineers who take end-to-end ownership - from understanding the problem and designing systems to implementing, deploying, and operating them in production.
A SNAPSHOT OF YOUR RESPONSIBILITIES
Design, build, and operate high-throughput, low-latency backend microservices for real-time personalization and content recommendations
Take complete ownership of assigned problem areas, from system design to production rollout and ongoing operations
Build and evolve distributed systems that are resilient, fault-tolerant, and observable in production
Work with multiple data stores including relational databases, vector databases, search engines, key-value stores, and caching systems to build efficient data access patterns
Integrate ML models into real-time serving pipelines, working closely with ML Engineers and Data Scientists
Perform high-level and low-level system design, clearly articulating trade-offs and architectural decisions
Optimize systems for performance, latency, scalability, and cost at scale
Collaborate with Product, Data Science, Machine Learning, Analytics, and Platform Engineering teams to deliver recommendation capabilities across Web, Mobile, and Connected TV
Participate in on-call rotation and drive production reliability and operational excellence
Contribute to engineering best practices around architecture, testing, CI/CD, observability, and security
WHAT YOU WILL NEED
Bachelor's or Master's degree in Computer Science or a related technical field
5+ years of experience building backend software systems in production environments
Strong programming skills in one or more backend languages, preferably Golang
Experience designing and building distributed systems with a focus on scalability, availability, and reliability
Hands-on experience with relational databases (PostgreSQL/Aurora) including query optimization and data modeling
Experience with caching systems (Redis/ElastiCache) and cache invalidation strategies
Experience working with NoSQL databases (DynamoDB) and understanding of access patterns and cost trade-offs
Hands-on experience with cloud-based systems on AWS
Solid understanding of data structures, algorithms, and their application in backend and distributed systems
Ability to work independently, take ownership, and deliver systems end-to-end with minimal guidance
NICE TO HAVE
Experience with vector databases (Milvus, Zilliz, Pinecone) and similarity search systems
Experience with search engines (OpenSearch/Elasticsearch)
Experience with ML model serving and real-time inference systems (Triton, KServe, SageMaker)
Experience building recommendation engines, personalization platforms, or content ranking services
Experience with event-driven architectures (SNS/SQS, Kafka)
Kubernetes and container orchestration (EKS, pod tuning, HPA)
Infrastructure as Code (Terraform)
Observability stack (OpenTelemetry, Prometheus, Grafana, Datadog)
Service mesh experience (Istio)
Experience with experimentation / A/B testing platforms
Experience with streaming platforms, digital media, or consumer-facing applications
#LI-SP1
We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, gender identity, disability, protected veteran status, or any other characteristic protected by law. We will consider for employment qualified applicants with criminal histories consistent with applicable law.
