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

Remotebridge; United States; Remoto; USD 240.000 - 350.000; Tempo pieno

Vantaggi offerti da questo lavoro

Equity compensation

Remote-first workforce

Health-insurance premium reimbursement

Descrizione del lavoro

micro1 is hiring a Member of Technical Staff to Realm Lab, focusing on frontier AI research. The role centers on evaluating and improving systems via benchmarks, RL environments, and high-quality datasets. You will collaborate with researchers and domain experts in a remote-first setting.

The position requires a strong CS/ML background and hands-on experience with ML evaluations, Python, and data pipelines, and offers a competitive USD salary with equity and bonuses.

Competenze

• Strong technical background in CS/ML with research focus.
• Hands-on experience designing ML evaluations and benchmarks.
• Proficiency in Python and data pipeline tooling.
• Understanding of LLMs and evaluation methodologies.

Mansioni

• Own research and evaluation initiatives end-to-end.
• Design evaluation capabilities, RL environments, benchmark suites.
• Develop datasets, task specifications, evaluation rubrics.
• Analyze model behavior via failure analysis and capability gaps.
• Collaborate with researchers to design experiments and calibrate evaluation quality.
• Build reproducible evaluation pipelines and contribute to open research artifacts.

Conoscenze

Strong technical background in CS/ML

Hands-on ML evaluation design

Python proficiency

LLM/foundation model understanding

Experiment design & analysis

Independent yet collaborative work

Strumenti

Python

Data pipelines

ML research infrastructure

Descrizione del lavoro

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

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

What We're Looking For

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

Preferred Qualifications

• Experience with reinforcement learning environments, reward modeling, simulators, or feedback-driven training systems.
• Experience evaluating computer use agents, tool use, browser automation, or multi-step agentic workflows.
• Familiarity with multimodal reasoning, long-context evaluation, instruction following, or deep research benchmarks.
• Experience with human-in-the-loop evaluations, expert-generated datasets, or post-training data.
• 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.