MirendilMember of Technical Staff, Recursive Self-Improvement (RSI)
Mirendil is hiring a Member of Technical Staff, Recursive Self-Improvement (RSI) in San Francisco, United States. It pays $300k a year and Level rates it ; you can apply on Mirendil.
Level's read on this job
- Builds AI ●●●● is how Level rates this role: the job is building AI systems.
- Tools and skills the posting cites include AI research, machine learning and deep learning.
- Level classifies it as a mid-level role.
- Level lists 14 other open roles at Mirendil.
AI in this role
Research Engineer focused on building autonomous AI systems that recursively improve how AI models are trained, evaluated, and operated.
Mirendil
Mirendil is a tech-first company focused on solving core bottlenecks that unlock step-change acceleration across science and technology. Our first goal is to democratize frontier AI R&D across scientific disciplines. We are building a frontier AI research company and training our own models end-to-end.
The Role
We are looking for an innovative, rigorous Research Engineer to join our team to accelerate AI self-improvement. This role requires a deep understanding of ML at both the application and system levels. You will ship AI-driven systems that recursively improve how AI systems are trained, evaluated, deployed, and operated at scale. If you are driven by the compounding potential of accelerating the AI loop, you will thrive in this role. Areas you might work on include:
Build autonomous AI that improves AI. Develop models, harnesses, and pipelines that automate parts of the ML lifecycle - data curation, training optimization, debugging, model selection, and experiment execution - and measure their impact on the speed and reliability of AI R&D.
Close loops across the stack. Identify the highest-leverage improvement opportunities anywhere in the stack, from distributed pre-training, post-training, and serving to agent harnesses, runtimes, and research environments, and jointly design and optimize across layers.
Run experiments end-to-end. Form hypotheses, design experiments, build the infrastructure to run them at scale, and turn results into shipped improvements.
Develop evaluation and observability. Build benchmarks, automated evals, and monitoring systems that surface regressions, failure modes, and emergent behaviors in AI systems.
If you're excited about closing the loop at scale, we'd love to hear from you.
We offer a base salary of $300,000–$400,000 USD and a meaningful equity grant, depending on experience and background, along with competitive benefits.
How we rate this
Member of Technical Staff, Recursive Self-Improvement (RSI) at Mirendil rates 100 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a research question you investigated. What did you find?
- Tell me about a project where machine learning was part of your work. What did you do?
- Tell me about a project where deep learning was part of your work. What did you do?
- Tell me about a project where distributed training was part of your work. What did you do?
- Tell me about a project where mlops was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: AI Research, Machine learning, Deep learning, Distributed Training, and MLOps. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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