Senior Software Engineer, AI Data Systems, Database Infrastructure

Jobtailor; Redwood City (CA); On-site; USD 170,000 - 250,000; Full time

Job summary

Jobtailor in Redwood City, CA seeks an experienced Database Platform Engineer to design, build, and operate scalable database infrastructure for mission-critical production and AI systems.

You will scale relational, analytical, and vector data stores, improve performance, and own architecture decisions around partitioning, replication, and caching. Collaborate with AI, backend, and security teams to ensure uptime and data integrity.

Qualifications

• 7+ years of experience in database infrastructure, backend infra, 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.

Responsibilities

• 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.

Skills

7+ years in database infra

Production-grade distributed systems

PostgreSQL/MySQL/Aurora

Analytical data stores (ClickHouse/Big

Vector databases/search systems

Partitioning, sharding, replication

Caching strategies (Redis, Memcached)

Python/Go/C++ programming

Cloud, Kubernetes, Terraform

Observability and SRE practices

Job description

Responsibilities

• 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.

Requirements

• 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.

Core Competencies

Demonstrates expertise in designing and operating scalable database infrastructure, with a focus on high availability, performance optimization, and data lifecycle management. Proficient in collaborating with cross‑functional teams to support AI and backend infrastructure needs.