Senior Software Engineer, AI Data Systems & Database Infrastructure

What you'll do

• Design, build, and operate scalable database infrastructure for mission-critical production and AI systems.
• Scale relational, analytical, and vector data stores to support growing product, customer, and AI workloads.
• Improve database performance across latency, throughput, availability, reliability, durability, and cost.
• Own database architecture decisions around partitioning, sharding, replication, indexing, caching, query optimization, and data modeling.
• Operate tier-0 data services with strong reliability, observability, incident response, and disaster recovery practices.
• Build automation and tooling to improve database provisioning, migrations, monitoring, backups, failover, and capacity planning.
• Partner with AI teams to support data infrastructure needs for embeddings, vector search, retrieval workflows, training data, model evaluation, and analytics.
• Build low-latency data-serving patterns that power AI features in production.
• Work closely with engineering teams to design data access patterns that are scalable, reliable, and performant.
• Identify bottlenecks in production systems and drive improvements across application, database, cache, and infrastructure layers.
• Define and enforce best practices for schema design, database usage, data lifecycle management, and operational safety.
• Help evolve our long-term data platform strategy as the company scales.

What you'll bring

• 7+ years of industry experience in database infrastructure, backend infrastructure, distributed systems, or production platform engineering.
• Deep hands-on experience operating and scaling production databases in high-availability environments.
• Strong experience with relational databases such as PostgreSQL, MySQL, Aurora, CockroachDB, Vitess, or similar systems.
• Experience with analytical data stores such as ClickHouse, BigQuery, Snowflake, Redshift, Druid, Pinot, or similar technologies.
• Experience with vector databases or vector search systems such as pgvector, Pinecone, Milvus, OpenSearch, or similar systems.
• Strong understanding of partitioning, sharding, replication, indexing, caching, query planning, and storage engine tradeoffs.
• Proven ability to optimize systems for low latency, high availability, reliability, and operational simplicity.
• Experience operating tier-0 or business-critical infrastructure services with strong uptime and reliability requirements.
• Strong understanding of caching strategies using systems such as Redis, Memcached, CDN-backed caches, or application-level caching.
• Experience with observability, monitoring, alerting, SLOs, capacity planning, and incident response for database systems.
• Strong programming skills, ideally in Python, C++, Go, or similar languages.
• Experience with cloud infrastructure, Kubernetes, Terraform, CI/CD, and infrastructure-as-code practices.
• Ability to collaborate effectively with backend, AI, product, security, and infrastructure teams.
• Strong ownership mindset and ability to make pragmatic tradeoffs in complex production environments.

Nice to Have

• Experience scaling databases for real-time, high-volume, customer-facing products.
• Experience with multi-region database architectures, replication, failover, disaster recovery, and data residency considerations.
• Experience with database migration strategies, online schema changes, zero-downtime migrations, and backfills.
• Experience supporting AI or ML workloads, including vector search, retrieval-augmented generation, embedding pipelines, feature stores, training data pipelines, or model evaluation systems.
• Experience with streaming systems such as Kafka, Flink, or Spark.
• Experience with database internals, storage engines, distributed consensus, or query execution.
• Experience managing cost and performance tradeoffs across cloud-managed and self-hosted database systems.
• Experience building internal database platforms, tooling, or paved paths for engineering teams.