Member of Technical Staff

About Plasma

AI is crossing a threshold. Systems of agents can now do consequential work unattended, for hours at a time, and most companies still lack the layer that would let them trust it. Plasma builds that layer: delegation with limits, live oversight, and one record of everything learned. Infrastructure for an AI operable world.

We come from places where autonomy already carried real stakes. Our founders built Omega Strategies, an autonomous trading firm that ran hundreds of probabilistic models against live capital. The team's experience spans Palantir, Subquadratic, D. E. Shaw, J.P. Morgan, Google, TPG, and Morgan Stanley, founding-team startup experience, and more.

The Opportunity

We're looking for a founding-level platform engineer to work alongside our CTO on Plasma's core infrastructure. You'll build the data pipelines, agent orchestration layers, API infrastructure, and reliability foundations that everything else runs on.

We're looking for a commercially minded, product-oriented engineer from a top AI lab or quantitative finance background. If you've spent time somewhere where the bar for production systems is unusually high and want to build something from scratch, this role is for you.

What You'll Work On

• Core Platform: Own the infrastructure (compute, storage, APIs, pipelines) Plasma's systems run on. Architectural decisions you make early will compound for a long time

• Agent Infrastructure: Build the orchestration, observability, and governance controls that let AI agents operate reliably in production

• Data Systems: Design scalable pipelines where correctness and auditability are requirements, not nice-to-haves

• Reliability & Observability: Establish the monitoring, alerting, and incident response practices that make Plasma's systems dependable

• Technical Bar: Set the standard for engineering quality and shape architectural decisions

What We're Looking For

• Deep expertise in distributed systems, backend infrastructure, or data engineering

• Experience building and operating data-intensive systems in production (pipelines, streaming, warehouses, enterprise orchestration, model serving)

• Familiarity with AI/ML infrastructure: LLM systems, agent frameworks

• Comfort with cloud infrastructure (AWS, GCP), modern DevOps practices, and large-scale enterprise integrations

• Background at an AI lab, top quantitative firm, or high-rigor infrastructure team is a strong plus

• Exceptional judgment about when to build, when to buy, and when to keep it simple