Senior Software Engineer, AI Data Systems & Database Infrastructure

Ambient.ai is the category creator and leader in Agentic Physical Security. Powered by Ambient Pulsar, the first reasoning Vision-Language Model purpose-built for physical security, our platform seamlessly integrates with existing security cameras and physical access control systems to unify monitoring, access control, threat assessment, response, and investigations through an always-on reasoning layer that augments security operators with superhuman capabilities. The results: 95% fewer false alarms, investigations 20x faster, and 10x faster response.

The momentum speaks for itself: we doubled new ARR in FY26, we process 200M+ video hours per day, and have delivered results for world-class customers including Cisco, ServiceNow, SentinelOne, TikTok, Bayer, and MoMA. That kind of momentum creates an environment where great people thrive, and it shows: we recently ranked #71 out of 500 on the Forbes best startup employers list.

Founded in 2017 and backed by Andreessen Horowitz, Y Combinator, and Allegion Ventures, Ambient.ai is on a fast-paced journey to fulfill our mission: prevent every security incident possible.

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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, CockroachDB, Spanner, DynamoDB, Cassandra, Redis, ClickHouse, 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.

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  Design, build, and operate scalable database infrastructure for mission-critical production and AI systems.

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  Scale relational, analytical, and vector data stores to support growing product, customer, and AI workloads.

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  Improve database performance across latency, throughput, availability, reliability, durability, and cost.

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  Own database architecture decisions around partitioning, sharding, replication, indexing, caching, query optimization, and data modeling.

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  Operate tier-0 data services with strong reliability, observability, incident response, and disaster recovery practices.

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  Build automation and tooling to improve database provisioning, migrations, monitoring, backups, failover, and capacity planning.

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  Partner with AI teams to support data infrastructure needs for embeddings, vector search, retrieval workflows, training data, model evaluation, and analytics.

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  Build low-latency data-serving patterns that power AI features in production

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  Work closely with engineering teams to design data access patterns that are scalable, reliable, and performant.

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  Identify bottlenecks in production systems and drive improvements across application, database, cache, and infrastructure layers.

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  Define and enforce best practices for schema design, database usage, data lifecycle management, and operational safety.

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  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, 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.

• 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.

You will be successful in this role if you can operate critical database systems with a high bar for reliability while continuously improving scale, performance, and developer velocity.

You should have a strong bias for operational excellence, a deep understanding of database tradeoffs, and a proven track record of scaling real production systems. You should be comfortable debugging complex latency issues, planning capacity before it becomes a problem, and designing systems that remain reliable as data volume, query complexity, and customer usage grow.

This role is ideal for someone who has done this work before: scaling production data stores, improving reliability, reducing latency, and supporting teams that depend on data infrastructure as the foundation for their products and AI systems.