Senior Software Engineer, AI Data Systems & Database Infrastructure
Senior Platform Engineer designing, building, and scaling database infrastructure for production and AI systems, including relational, analytical, and vector stores. Requires 7+ years experience with distributed systems, high-availability databases, and supporting AI workloads like vector search and RAG.
About the job
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.
Skills
Postgres, MySQL, Cockroachdb, Vitess, ClickHouse, BigQuery, Snowflake, Redshift, Pgvector, Pinecone, Milvus, Opensearch, Redis, Kubernetes, Terraform
Similar jobs
Data Engineering jobs
Vercel
San Francisco, CAOwns the Finance data infrastructure supporting billing, usage-based revenue, forecasting, reporting, and close. The role requires production data engineering experience, strong SQL and Python, dbt and orchestration expertise, Finance-domain fluency, and the ability to mentor engineers and partner with business stakeholders.
Vercel
San Francisco, CAOwns end-to-end GTM data pipelines, transformations, and models that power reliable pipeline, revenue, attribution, and funnel reporting. The role requires senior-level data engineering experience, strong SQL and Python, dbt and orchestration expertise, GTM metric fluency, and stakeholder partnership skills.
Bevi
Boston, MA
Own and evolve Bevi’s end-to-end data platform, from ingestion and IoT modeling through governed self-service analytics and AI access. The senior individual contributor will architect scalable streaming and batch systems, establish governance and observability, and provide technical leadership across the Data & Data Science organization.
Clay
New York, NYBuild customer-facing data products and shared platform systems that transform conflicting, constantly changing sources into reliable, searchable information. The role requires 8+ years of hands-on engineering experience, strong Python and SQL skills, and ownership of product quality, reliability, and delivery.
Deepgram
United States
Own the company’s metric governance program by defining canonical metrics, enforcing them in semantic and catalog systems, improving data quality, and validating AI-agent outputs. Requires 5+ years in analytics or analytics engineering, strong SQL, production semantic-layer ownership, and experience with AI evaluation and data governance.