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Agent Engineer Intern
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Vecna AI
Chicago, IL (In Person)
Full-Time
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Job Description
Agent Engineer Intern at Vecna AI Agent Engineer Intern at Vecna AI in Chicago, Illinois Posted in 9 days ago.
Single-task agents are table stakes. The hard problem is the layer above them: how dozens of agents coordinate, hand off work, share context, recover from failure, and stay aligned with a strategic objective across hours of autonomous operation. Most agent systems collapse somewhere between step 50 and step 200 context gets corrupted, plans drift, tools fail silently. We're building the system that doesn't, and you'll own a real, scoped piece of it.
This is a hands-on engineering internship working directly with the founding team. You'll ship code that goes into the platform, not a side project that gets thrown away at the end.
What You'll Work OnYou'll take ownership of a focused project within one of these areas:
Multi-agent orchestration supervisor and worker patterns, role-based delegation, sub-agent spawning, and the coordination primitives that let agents collaborate without stepping on each other.
Procedural environment generation the harnesses that generate synthetic gyms and self-mutating environments programmatically.
Async event bus and message passing the substrate over which agents publish, subscribe, hand off work, and react to environmental changes, with retries and ordering guarantees that hold up under load.
Context management long-horizon coherence through summarization, relevance scoring, pruning, and memory externalization across sessions and worker boundaries.
Persistent memory and bitemporal graph state knowledge and spatial graphs that model the operational environment, asset relationships, and cross-session state, tracking both when a fact was true and when the system learned it so agents can reason about state as of any point in time.
Tool design and execution environments browser automation, computer use, sandboxed terminals, and code interpreters that agents can invoke safely.
Self-reflection and recovery agents that detect their own failures, critique their reasoning mid-task, backtrack, and retry with improved strategies instead of failing silently or compounding a bad plan.
Self-improvement loops agents that learn from their own trajectories: mining past runs for what worked, distilling successful strategies into reusable behavior, and getting better at a task across episodes rather than starting cold each time.
Long-horizon planning and reasoning OODA-loop execution, dynamic plan decomposition, confidence-gated action selection, and the architectures that keep an agent aligned with a strategic objective across hours of operation and hundreds of steps without drifting.
Who Thrives HereWe care less about pedigree than evidence.
Break things, then stabilize them Robustness in long-horizon agent systems isn't a feature you add at the end; it's the product. You push systems until they fail, study the failure honestly, and rebuild stronger. We don't punish breakage that produces learning we punish hiding it.
Try harder When the obvious approach fails, you find the second, third, and fifth. You re-read the paper, instrument the system, ask a smarter question. Most ceilings here are self-imposed.
You Might Be a Fit If YouAre pursuing a BS, MS, or PhD in computer science, engineering, or a related field or are skipping that path and have the repos to back it up.
Have built something real with LLMs or agents a project, research prototype, open-source contribution, or hackathon build you can walk us through.
Are strong in Python, comfortable with async, and have written code other people depend on.
Have exposure to one or more of: distributed systems, event-driven architectures, simulation or procedural environment generation, long-horizon planning or self-reflection loops, graph data models, or tool/API integration.
Operate with high agency, hold strong opinions you update quickly, and are comfortable in ambiguity.
A lodging stipend is provided for those outside Chicago. recblid v2wrm9wy7a3xefnr9utib766x57n3p
Type:
internJob Description:
The RoleYou'll spend the term building inside the intelligence layer between our models and the real world. Vecna's Virtual Workers execute long, multi-step operations across hundreds of tools without losing coherence, context, or intent and you'll work alongside our engineers on the orchestration, memory, tool design, and reasoning architectures that make that possible.Single-task agents are table stakes. The hard problem is the layer above them: how dozens of agents coordinate, hand off work, share context, recover from failure, and stay aligned with a strategic objective across hours of autonomous operation. Most agent systems collapse somewhere between step 50 and step 200 context gets corrupted, plans drift, tools fail silently. We're building the system that doesn't, and you'll own a real, scoped piece of it.
This is a hands-on engineering internship working directly with the founding team. You'll ship code that goes into the platform, not a side project that gets thrown away at the end.
What You'll Work OnYou'll take ownership of a focused project within one of these areas:
Multi-agent orchestration supervisor and worker patterns, role-based delegation, sub-agent spawning, and the coordination primitives that let agents collaborate without stepping on each other.
Procedural environment generation the harnesses that generate synthetic gyms and self-mutating environments programmatically.
Async event bus and message passing the substrate over which agents publish, subscribe, hand off work, and react to environmental changes, with retries and ordering guarantees that hold up under load.
Context management long-horizon coherence through summarization, relevance scoring, pruning, and memory externalization across sessions and worker boundaries.
Persistent memory and bitemporal graph state knowledge and spatial graphs that model the operational environment, asset relationships, and cross-session state, tracking both when a fact was true and when the system learned it so agents can reason about state as of any point in time.
Tool design and execution environments browser automation, computer use, sandboxed terminals, and code interpreters that agents can invoke safely.
Self-reflection and recovery agents that detect their own failures, critique their reasoning mid-task, backtrack, and retry with improved strategies instead of failing silently or compounding a bad plan.
Self-improvement loops agents that learn from their own trajectories: mining past runs for what worked, distilling successful strategies into reusable behavior, and getting better at a task across episodes rather than starting cold each time.
Long-horizon planning and reasoning OODA-loop execution, dynamic plan decomposition, confidence-gated action selection, and the architectures that keep an agent aligned with a strategic objective across hours of operation and hundreds of steps without drifting.
Who Thrives HereWe care less about pedigree than evidence.
Three traits are non-negotiable:
Hacker mindset You see systems as something to take apart and understand, not consume as designed. You read the source, poke at edges, and assume there is a faster or more correct way. The instinct to question how things work is the floor.Break things, then stabilize them Robustness in long-horizon agent systems isn't a feature you add at the end; it's the product. You push systems until they fail, study the failure honestly, and rebuild stronger. We don't punish breakage that produces learning we punish hiding it.
Try harder When the obvious approach fails, you find the second, third, and fifth. You re-read the paper, instrument the system, ask a smarter question. Most ceilings here are self-imposed.
You Might Be a Fit If YouAre pursuing a BS, MS, or PhD in computer science, engineering, or a related field or are skipping that path and have the repos to back it up.
Have built something real with LLMs or agents a project, research prototype, open-source contribution, or hackathon build you can walk us through.
We want builder evidence:
shipped systems, repos, papers not slide decks.Are strong in Python, comfortable with async, and have written code other people depend on.
Have exposure to one or more of: distributed systems, event-driven architectures, simulation or procedural environment generation, long-horizon planning or self-reflection loops, graph data models, or tool/API integration.
Operate with high agency, hold strong opinions you update quickly, and are comfortable in ambiguity.
A lodging stipend is provided for those outside Chicago. recblid v2wrm9wy7a3xefnr9utib766x57n3p