hiloop

Autoresearch as a service: we solve your hardest measurable problems

2.7Score#440 of 999 agents#2 of 4 in ML engineer

Why it ranks #440

hiloop, an AI ML engineer from YC S26, ranks #440 of 999 AI agents and #2 of 4 in ML engineer, with a score of 2.7 of 10. Data as of 3 Oct 2026.

The score adds four parts, each 0 to 10, by weight. Every point comes from public evidence. How scoring works ·

Proof 0 × 30% 0.0
Scale 1 × 30% 0.3
Momentum 6 × 25% 1.5
Autonomy 6 × 15% 0.9
= 2.7

Proof
Do customers use it? 30% of the score
0
No public claims of customers or revenue yet.
Not found: revenue or growth (+4), named customers (+3), a customer result (+2), a customer count (+2), work done at scale (+2)
Scale
Is there a real company behind it? 30% of the score
1
2 people.
- +1
  2 people on its YC page2 people
Not found: funding round in its news (+3)
Momentum
Is it shipping and growing now? 25% of the score
6
Shows a recent launch, a new claim and a new batch.
- +3
  Launched in the last 6 monthshiloop: we run thousands of experiments to improve your modelsLaunch YC post · 29 Jul 2026
- +2
  A new traction claim
  “They ran 4,188 experiments in two days and beat the published state of the art on Karpathy's autoresearch benchmark.”
  Launch YC post · 29 Jul 2026
- +1
  New: YC Summer 2026
Not found: open roles on YC (+2), funding news in the last year (+2)
Autonomy
How much of the job does it do on its own? 15% of the score
6
Supervised agent: takes actions in other systems; a person approves key steps.
- +6
  Level 2 of 4: Supervised agentTakes actions in other systems; a person approves key steps.
What the model read
- Acts in other systems: 54% likely
- Finishes whole tasks: 22% likely
- Calls itself an agent: 88% likely
Level 2: Takes actions in other systems; a person approves key steps.

About hiloop

hiloop helps teams train agents for tasks where general models are not good enough. Give us a task, your current agent or model, and an evaluation. hiloop runs an autoresearch campaign across data, SFT and other post-training methods, continual learning, prompts, tools, harnesses, and systems, then returns the best verified improvement. It runs hosted or in your cloud.

We provide the research system around models: persistent memory, full experiment lineage, compute orchestration, and statistical verification. We’re starting with agent and model training, continual learning, and optimization.

Reach out to us for early access at founders@hiloop.ai.