About the role
structured by ORIArgon · New York City · In person Argon builds AI software for life-sciences teams doing complex research, analysis, and writing. Our products connect documents, scientific evidence, and company knowledge to help people produce useful, well-supported work.
What you will do
- Construct a model of pharma. Make relevant evidence accessible through bespoke ontologies, indexing, filtering, and lexical and semantic search. Measure quality alongside latency and cost.
- Deliver backend systems end to end. Translate product and infrastructure needs into practical plans, write production code, and follow through until it works for customers. Break large projects into useful releases.
- Own infrastructure and reliability. Build and operate services on AWS and Kubernetes. Improve deployment tooling, observability, recovery, capacity management, and tenant isolation.
- Build our next search platform. Lead the search database evaluation and migration, including indexing, synchronization, query APIs, backfills, rollout, and rollback.
- Make asynchronous workloads dependable. Build systems that handle retries, duplicate work, rate limits, and partial failure. Extend the platform to support longer-running AI workflows.
What they are looking for
- A track record of personally delivering substantial search, backend, or infrastructure projects into production. You can explain what you built, how you shipped it, what failed, and what improved.
- Strong engineering fundamentals and comfort moving between application code, database queries, and infrastructure.
- Experience operating stateful or distributed services, including queues, concurrency, migrations, and recovery.
- Production experience with search, indexing, or retrieval systems, including the effects of freshness, filters, and permissions on results.
- Practical judgment: you scope work, choose a useful first version, communicate risks early, and finish the rollout.
- Clear communication and thoughtful collaboration with engineers, product partners, and domain experts.
Nice to have
- Hybrid search, relevance evaluation, private customer deployments, and AI workflow infrastructure are useful additional experience.
Benefits
- Direct ownership of important systems
- Work closely with the founders
- See the customer impact of your decisions
- Support relocation
Full posting text
Argon · New York City · In person
Argon builds AI software for life-sciences teams doing complex research, analysis, and writing. Our products connect documents, scientific evidence, and company knowledge to help people produce useful, well-supported work.
We’re hiring an experienced backend engineer to own the platform and search systems behind those products. You’ll work directly with the founders and a small engineering team, make consequential technical decisions, and carry projects from an ambiguous problem through implementation, rollout, and production operation.
WHAT YOU’LL DO
- Construct a model of pharma. Make relevant evidence accessible through bespoke ontologies, indexing, filtering, and lexical and semantic search. Measure quality alongside latency and cost.
- Deliver backend systems end to end. Translate product and infrastructure needs into practical plans, write production code, and follow through until it works for customers. Break large projects into useful releases.
- Own infrastructure and reliability. Build and operate services on AWS and Kubernetes. Improve deployment tooling, observability, recovery, capacity management, and tenant isolation.
- Build our next search platform. Lead the search database evaluation and migration, including indexing, synchronization, query APIs, backfills, rollout, and rollback.
- Make asynchronous workloads dependable. Build systems that handle retries, duplicate work, rate limits, and partial failure. Extend the platform to support longer-running AI workflows.
- Help the team ship faster. Improve service boundaries, shared libraries, tests, error handling, and development tools where they solve concrete problems.
WHAT YOU BRING
- A track record of personally delivering substantial search, backend, or infrastructure projects into production. You can explain what you built, how you shipped it, what failed, and what improved.
- Strong engineering fundamentals and comfort moving between application code, database queries, and infrastructure.
- Experience operating stateful or distributed services, including queues, concurrency, migrations, and recovery.
- Production experience with search, indexing, or retrieval systems, including the effects of freshness, filters, and permissions on results.
- Practical judgment: you scope work, choose a useful first version, communicate risks early, and finish the rollout.
- Clear communication and thoughtful collaboration with engineers, product partners, and domain experts.
Our backend stack includes TypeScript/Node.js, Python, PostgreSQL, AWS, and Kubernetes. Experience with comparable systems transfers. Hybrid search, relevance evaluation, private customer deployments, and AI workflow infrastructure are useful additional experience.
You’ll have direct ownership of important systems, work closely with the founders, and see the customer impact of your decisions. We work together in NYC and support relocation.