Senior Software Engineer, AI Data Systems & Database Infrastructure
About The Position
Ambient.ai is seeking a Senior Platform Engineer to design, build, and scale the database platform that powers its critical production and AI systems. This role involves working at the intersection of databases, distributed systems, application architecture, and AI infrastructure. The engineer will scale relational, analytical, and vector data stores, focusing on partitioning, sharding, routing, caching, replication, query optimization, and high-availability operations. A key aspect of the role is supporting AI teams by building data infrastructure for vector search, retrieval-augmented generation, embedding stores, model evaluation datasets, and low-latency data access for AI-powered products. The ideal candidate has experience operating database platforms at scale and understands the tradeoffs of various distributed systems. This position is suited for someone who wants to apply deep database and distributed systems expertise to next-generation AI-powered products.
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.
Nice To Haves
- 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, Flin, 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.
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.
Benefits
- Stock options
- Comprehensive health + welfare package (Medical, Dental, Vision, Life, EAP, Legal Services, 401k plan)
- Flexible time off
- Winter Break (time off between Christmas and New Year’s for most roles, depending on customer demand)
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
