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Machine Learning Researcher - Autonomous Agents

qwntl labs San Francisco Bay Area
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AI Summary

Join QWNTL Labs to research and build autonomous agent systems that operate reliably over long horizons, working on memory, alignment, and self-improvement. Own research questions around agent reliability, multi-agent coordination, and learned behavior evolution with access to $10M+ compute. Requires strong Python, research engineering depth, and a defensible track record of empirical ML work including papers or production systems.

Key Highlights
Venture-backed research lab in San Francisco studying latent state and agent reliability over long horizons
Access to $10M+ compute and a private agent harness for building autonomous workers
Work directly with CoFounder & CEO Lindell Cumes on defensible research results
Hybrid role in London or San Francisco (3/3)
Compensation of $600k + equity with visa sponsorship available
Key Responsibilities
Research and build autonomous systems that can operate reliably for months, accumulate knowledge, and improve without losing objectives
Investigate agent memory, alignment at inference, multi-agent coordination, and reliable self-improvement
Own research questions around what survives when agents compress experience and how learned behavior changes over millions of interactions
Produce defensible empirical results with working systems
Ship research code as part of production-quality engineering
Technical Skills Required
Python Research Engineering Machine Learning
Benefits & Perks
$600k + equity compensation
Visa sponsorship available
Hybrid work in London/San Francisco
Nice to Have
Published work on agents, long-context behavior, evaluation methodology, reliability, or alignment
Experience with production LLM systems including telemetry, tracing, and incident analysis
Background in survival analysis, time-to-event statistics, or experimental design
Experience with post-training, agent memory, or long-running systems

Job Description


To Apply

The application starts with access to what we're building:

  1. Apply at qwntl.com/early-access.
  2. If selected, you'll get access to the private agent harness our team uses to build and manage autonomous workers, with support for 30-day operation.
  3. Build something with it and submit your work through the team Discord, shared after acceptance.


About QWNTL Labs

QWNTL Labs is a venture-backed research lab in San Francisco studying latent state and working on agent reliability over long horizons.

We're building autonomous systems that can work for months, accumulate knowledge and improve without losing their objectives. Agents whose earlier decisions become the conditions they have to operate under weeks later, whose memory has been rewritten repeatedly, whose mistakes have consequences beyond the current context window.


Making that work reaches into memory, alignment at inference, multi-agent coordination and reliable self-improvement. What survives when an agent compresses its experience? How does learned behaviour change over millions of interactions? Can a system improve itself while preserving the constraints that make it useful?


You'll own those questions through working systems and defensible results, with >$10M of available compute, working with CoFounder & CEO Lindell Cumes.


We're looking for people with serious empirical work behind them: a paper, an evaluation or a production ML system you can defend down to the failures. Strong Python and research engineering matter. Experience with post-training, agent memory or long-running systems is particularly relevant.


Strong candidates may also have

  • Published work on agents, long-context behavior, evaluation methodology, reliability, or alignment
  • Experience with production LLM systems, including telemetry, tracing, and incident analysis
  • Background in survival analysis, time-to-event statistics, or experimental design
  • Software engineering depth. Research code here ships


Logistics

  • Location: London/San Francisco, 3/3 hybrid
  • Compensation: $600k + equity
  • Visa sponsorship: yes
  • Start: as soon as we find the right person




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