Member of Technical Staff, Realm Lab (frontier AI research)

micro1 Inc.; United States; Remote; USD 240,000 - 350,000; Full time

Benefits offered by this job

Equity compensation

Bonus opportunities

Health insurance premium reimbursement

401(k) with company match

Paid time off

Remote-first workforce

Job summary

micro1 is seeking a Member of Technical Staff for Realm Lab to drive frontier AI research initiatives from a remote setting in the United States. This hands-on role sits at the intersection of ML research, evaluation engineering, and data science to build benchmarks, RL environments, and high‑quality datasets that advance model capabilities.

You will collaborate with researchers and domain experts to design experiments, validate evaluations, and develop reproducible evaluation pipelines,

Qualifications

• Strong background in computer science, ML, or AI.
• Hands-on experience designing ML evaluations and benchmarks.
• Proficient in Python and building evaluation tools.

Responsibilities

• Own research and evaluation initiatives end-to-end.
• Design and validate capability evaluations and benchmark suites.
• Develop datasets, task specs, evaluation rubrics.
• Analyze model behavior to identify capabilities gaps.
• Collaborate with researchers to design experiments.
• Build reproducible evaluation pipelines and feedback loops.
• Contribute to benchmarks, papers, and open artifacts.

Skills

Machine Learning

Evaluation research

Python

Data pipelines

LLMs understanding

Experiment design

Education

BS/MS in CS/ML

Tools

Python

Benchmark suites

RL environments

Data pipelines

Job description

Job Title: Member of Technical Staff, Realm Lab (frontier AI research)

Job Type: Full time

Location: Remote

The Role

We're hiring a Member of Technical Staff (MTS) to join Realm Lab and drive research initiatives focused on evaluating and improving frontier AI systems.

This is a hands‑on technical role at the intersection of machine learning, data research, and evaluation engineering. You'll work closely with researchers and domain experts to develop benchmarks, reinforcement learning environments, and high‑quality datasets that advance model capabilities and performance.

What You’ll Do

1. Own research and evaluation initiatives end‑to‑end, from task design and dataset development to benchmark validation and performance analysis.
2. Design and validate capability evaluations, RL environments, and benchmark suites for computer use, agentic workflows, and emerging model capabilities.
3. Develop high‑quality datasets, task specifications, evaluation rubrics, and methodologies to improve post‑training data quality and model assessment.
4. Analyze model behavior through failure analysis, error taxonomies, and capability gap identification to inform research directions.
5. Collaborate with researchers and domain experts to design experiments, calibrate evaluation quality, and improve model performance.
6. Build reproducible evaluation pipelines and feedback loops to support iterative model development.
7. Contribute to benchmarks, research papers, technical reports, and open research artifacts.

What We’re Looking For

1. Strong technical background in computer science, machine learning, AI, or a related field.
2. Hands‑on experience designing ML evaluations, benchmark suites, research datasets, or experimental frameworks.
3. Proficiency in Python and experience building data pipelines, evaluation tools, or ML research infrastructure.
4. Understanding of LLMs, foundation models, post‑training techniques, and evaluation methodologies.
5. Ability to translate complex real‑world workflows into structured tasks, measurable criteria, and research experiments.
6. Strong analytical skills in model failure analysis, experimental design, and evaluation quality.
7. Ability to work independently and collaborate effectively with researchers, engineers, and domain experts.

Preferred Qualifications

1. Experience with reinforcement learning environments, reward modeling, simulators, or feedback‑driven training systems.
2. Experience evaluating computer use agents, tool use, browser automation, or multi‑step agentic workflows.
3. Familiarity with multimodal reasoning, long‑context evaluation, instruction following, or deep research benchmarks.
4. Experience with human‑in‑the‑loop evaluations, expert‑generated datasets, or post‑training data.
5. Contributions to AI research publications, open‑source benchmarks, or evaluation frameworks.

Compensation & Benefits Notice

The national pay range for this full‑time position is base salary of $240,000 –$350,000. All employees are eligible for equity compensation, and employees may also receive performance‑based bonuses, dependent on role and subject to company policies. micro1 provides a comprehensive benefits package, including up to 100% reimbursement for health‑insurance premiums, paid time off, a 401(K) plan with a company match, and additional benefits designed to support a high‑performing, remote‑first workforce.

micro1 is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, veteran status, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation during the application process, reach out to [e-mail].

Our hiring process utilizes artificial intelligence tools to assist in candidate screening and assessment. Our AI tools are designed to complement, not replace, human decision‑making.

Disclaimer

The information contained in this job posting, including but not limited to role responsibilities, qualifications, compensation, and benefits, is provided for informational purposes only and does not constitute a binding offer of employment. micro1 reserves the right to amend, modify, or withdraw any portion of this posting at its sole discretion and without prior notice. All employment decisions are made in accordance with applicable laws and regulations.