Senior Software Engineer, AI Data Systems & Database Infrastructure

Ovii's Interpretation of the Role

Ambient.ai seeks a senior engineer to design, build, and operate high‑scale database infrastructure that powers both core production services and AI‑driven features. The role blends deep database expertise with distributed‑systems know‑how to support relational, analytical, and vector data stores in a tier‑0 environment.

Role Snapshot

• Senior Platform Engineer
• Database infrastructure focus
• AI data systems
• Hybrid (Redwood City)
• Full‑Time
• 7+ years experience

Must-Have Requirements

• Relational databases (PostgreSQL, MySQL, Aurora, CockroachDB, Vitess)
• Analytical data stores (ClickHouse, BigQuery, Snowflake, Redshift)
• Vector databases (pgvector, Pinecone, Milvus, OpenSearch)
• Caching systems (Redis, Memcached)
• Programming languages (Python, Go, C++)
• Cloud/Kubernetes/Terraform/CI‑CD
• Observability, monitoring, SLOs, capacity planning
• Distributed‑systems concepts (partitioning, sharding, replication, indexing, query planning)
• database infrastructure
• distributed systems
• production platform engineering
• AI data systems

Nice-to-Have Signals

• Real‑time high‑volume scaling for customer‑facing products
• Multi‑region database architectures and disaster recovery
• Zero‑downtime migration and online schema change strategies
• Support for AI/ML workloads (embeddings, feature stores)
• Streaming platforms (Kafka, Flink, Spark)
• Database internals and storage‑engine knowledge
• Cost‑performance trade‑off management across cloud and self‑hosted databases
• Building internal platform tooling
• real‑time high‑volume scaling
• multi‑region architectures
• AI/ML workload support

Work Setup

• Location: Redwood City, United States
• Work mode: HYBRID
• Remote scope: UNSPECIFIED
• Employment type: Full-Time

Not Specified in JD

• Visa sponsorship
• Salary range
• Remote eligibility
• Coding test

What You'll Likely Work On

• Design, build, and operate scalable database platforms for production and AI workloads
• Scale relational, analytical, and vector stores while improving latency, throughput, and cost
• Own architecture decisions: partitioning, sharding, replication, indexing, caching, query optimization
• Run tier‑0 data services with strong reliability, observability, incident response, and disaster recovery
• Create automation for provisioning, migrations, backups, failover, and capacity planning
• Partner with AI teams to support embeddings, vector search, training data, and model evaluation pipelines
• Develop low‑latency data‑serving patterns for AI‑powered features
• Define best practices for schema design, data lifecycle, and operational safety

Good Fit If You Have

• Strong ownership mindset and pragmatic trade‑off decisions
• Proven ability to debug latency issues and plan capacity
• Effective collaboration across backend, AI, product, security, and infra teams
• Experience scaling production data stores in high‑availability environments