About Helios
Helios is building a new kind of company to solve America’s hardest problems, starting with the government interaction layer.
Government shapes every consequential market, but the infrastructure connecting public institutions and private organizations remains fragmented, manual, and difficult to navigate. Helios is rebuilding that layer.
Our core platform, Proxi, gives organizations the intelligence they need to understand what government is doing, why it matters, and what to do next. From that foundation, we design and deploy secure, mission-specific systems for government agencies, enterprises, and institutions operating in complex and highly regulated environments.
We bring together frontier AI, deep public-sector expertise, and forward-deployed execution. Our team includes leaders and builders from the White House, U.S. Department of State, Datadog, and Microsoft. We are backed by leading institutional investors and trusted by organizations working on high-stakes problems across government and industry.
Mission
As a distributed systems engineer at Helios you will expand and operate the distributed execution, storage, scheduling, and reliability primitives that support data ingestion, document processing, search indexing, model inference, agents, and continuously running research workflows.
The primary responsibility centers around the orchestration of critical infrastructure to meet mission-critical performance and reliability guarantees. Our platform runs a multitude of network crawling, compute grids, memory-heavy document processing, OCR, inference, and latency-sensitive jobs, all of which you should be familiar with. Preferred areas of expertise include:
- Queue-, log-, workflow-, and actor-based distributed execution systems, including Kafka, Redpanda, Pulsar, SQS, Pub/Sub, RabbitMQ, Temporal, and equivalent technologies.
- Establishment of at-least-once delivery with effectively once-only business outcomes through transactional outbox and inbox patterns, sagas, reconciliation, dead-letter handling, replay and historical backfill procedures.
- CPU-, memory-, disk-, network-, and GPU-aware scheduling, including workload classification, priority allocation, starvation prevention, tenant and dependency concurrency limits, placement constraints, resource quotas, and noisy-neighbor isolation.
- PostgreSQL, object storage, Redis or equivalent caches, search indices (Elasticsearch or Typesense), vector stores, change-data-capture, and graph-storage systems.
- Application of distributed coordination and concurrency controls, including leader election, locks, leases, fencing tokens, optimistic concurrency, conflict resolution, and partition recovery.
- Provisioning and operation of AWS, GCP, or Azure infrastructure through terraform or similar IaC, vulnerability scanning, CI/CD and deployment methods.
- Definition and administration of SLO/Is, error budgets, release controls, metrics, OpenTelemetry, Datadog, fault injection and load/failure testing.
- Evaluation of system performance and cost via e2e latency decomposition, resource and dependency profiling.
- Enforcement of platform security and tenant isolation policies.
The key objective of this work is to support the real-time access and availability of our entire data corpus as well as supporting the continued construction of the Helios Rapid Ontology System (H.R.O.S.), our long horizon memory data plane.
How we work
- Own outcomes, not just assigned tasks. Identify what needs to happen and drive it through completion.
- Move quickly without lowering the standard. Speed and rigor are complementary.
- Stay close to the mission and the user. The best decisions begin with the real problem.
- Work across boundaries. Everyone contributes beyond the narrow limits of a job title.
- Communicate directly. We value clear thinking, honest feedback, and low-ego collaboration.
- Build for the real world. Our systems must perform in complex, regulated, and high-stakes environments.