Senior Product Engineer

present of AI; New York (NY); Presencial; USD 170.000 - 230.000; Jornada completa

Descripción de la vacante

Paradigm is seeking a hands-on engineer to build and run distributed services in production. You will lead several sizable initiatives as the main contributor, applying systems thinking to complex tradeoffs and debugging tough issues with peers.

The role centers on reliable orchestration of large agent fleets, focusing on race conditions, throughput, and memory limits. You will work closely with teams to shape architectures near the AI core and scale workflows.

Formación

• Experience building and running distributed services in production.
• Ability to reason about data consistency at scale.
• Strong debugging skills for hard technical problems.
• Clear communication and collaboration across teams to align tradeoffs.

Responsabilidades

• Build and operate distributed services in production.
• Lead multiple sizable projects as the main contributor.
• Design for race conditions, throughput, and memory limits.
• Collaborate across teams to align technical tradeoffs and roadmaps.

Conocimientos

Distributed systems

Concurrency

Debugging complex problems

Technical communication

Herramientas

Go

Redis

PubSub

RPC

Postgres

S3

Temporal

Descripción del empleo

The role asks for a hands on engineer who has spent years building and running distributed services in production, and who has led several sizable projects as the main contributor. A strong candidate brings deep systems thinking, comfort with concurrency and data consistency at scale, and the ability to debug hard problems that peers rely on. Clear communication and the patience to align people on difficult technical tradeoffs matter as much as coding skill.

How it fits the AI era

The product runs large fleets of agents in parallel, so the engineering work centers on making that orchestration reliable, fast, and observable. AI shifts much of the routine coding and research grunt work, but the hard parts stay human: designing for race conditions, throughput, and memory limits, and judging what to build. The role sits close to the AI itself, shaping the infrastructure that makes agent scale usable.

How it builds AI experience

Working here means gaining direct exposure to running agents at very high volume, including the concurrency, streaming, and data model problems that come with it. Candidates can build relevant experience by working on distributed systems, message passing, and realtime data pipelines, and by getting familiar with the listed tools such as Go, Redis, PubSub, RPC, Postgres, S3, and Temporal. Understanding how LLM driven workflows behave under load is a useful complement to that systems background.

Worth showing

• Distributed systems in production
• Concurrency and race conditions
• Primary contributor on large projects
• Clear technical communication

This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.

About Paradigm

AI research and data enrichment workspace utilizing agentic spreadsheets for market intelligence and go-to-market teams, per the company.