Senior Software Engineer, AI Data Systems & Database Infrastructure

Build a safer world with us, one incident at a time.

We are looking for a Senior Platform Engineer to design, build, and scale the database platform that powers our most critical production and AI systems.

We are looking for an engineer who deeply understands databases and distributed systems, and who can build the platforms, abstractions, and scaling patterns required to operate data stores reliably at high scale.

In this role, you will work at the intersection of databases, distributed systems, application architecture, and AI infrastructure. You will help scale relational, analytical, and vector data stores across both the database layer and the application layer. This includes designing systems for partitioning, sharding, routing, caching, replication, query optimization, and high‑availability operations.

You will also work closely with AI teams to build the data infrastructure that supports modern AI applications, including vector search, retrieval‑augmented generation, embedding stores, model evaluation datasets, analytical workloads, and low‑latency data access for AI‑powered product experiences.

The ideal candidate has built or operated database platforms at scale and understands the tradeoffs behind systems like Vitess, Cockroach DB, Spanner, DynamoDB, Cassandra, Redis, Click House, and modern vector search systems. You should be comfortable reasoning about latency, availability, durability, consistency, reliability, and operational complexity in tier‑0 production environments.

This role is ideal for someone who wants to apply deep database and distributed systems expertise to the next generation of AI‑powered products.

• 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.

• 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, Cockroach DB, Vitess, or similar systems.
• Experience with analytical data stores such as Click House, Big Query, Snowflake, Redshift, Druid, Pinot, or similar technologies.
• Experience with vector databases or vector search systems such as pgvector, Pinecone, Milvus, Open Search, 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…