Member of Technical Staff, Frontier AI

Micro1; India; A distancia; INR 16.901.000 - 30.047.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Equity compensation

Remote-first workforce

401K plan

Descripción de la vacante

micro1 is seeking a Staff-level MTS to own research and evaluation at the crossroads of research, data, and deployed AI systems. You will focus on rigorous evaluation, failure analysis, and iterative development to drive improvements in real-world performance.

As a hands-on contributor, you will design ML data systems, define datasets and evaluation rubrics, and work with researchers and operators to translate experiments into credible, deployed signals.

Formación

• Design robust ML evaluation datasets and rubrics.
• Translate real-world behavior into structured evaluation plans.
• Partner with researchers to calibrate data quality.

Responsabilidades

• Own research and evaluation initiatives end-to-end including problem framing and data design.
• Design ML-oriented data systems and pipelines for downstream model performance.
• Analyze failures to identify root causes and improvement opportunities.
• Translate real-world behavior into evaluation frameworks and data categories.
• Collaborate with researchers to calibrate quality and raise signal strength.

Conocimientos

Research signal quality

ML dataset design

Evaluation frameworks

QA processes

Ambiguity tolerance

Written and verbal comms

Stakeholder alignment

Descripción del empleo

Job Title

Member of Technical Staff, Frontier AI

Job Type

Full time

Location

Remote

The Role

We’re hiring a Member of Technical Staff (MTS) to act as a technical owner operating at the intersection of research, data, and real-world AI systems. This is a hands-on role focused on improving model and system performance through rigorous evaluation, failure analysis, and iterative development. You’ll work closely with researchers, domain experts, and operators to ensure that experimental work produces clean, defensible research signal—and that this signal translates into meaningful improvements in deployed systems.

What You’ll Do

• Own research and evaluation initiatives end-to-end: problem framing, data design, quality calibration, and signal validation.
• Design ML-oriented data systems, including task definitions, annotation schemas, rubrics, incentives, and pipelines optimized for downstream model performance.
• Analyze model and system failures to identify root causes, edge cases, and opportunities for improvement.
• Translate ambiguous, real-world behavior into structured evaluation frameworks and new data categories.
• Work closely with researchers and domain experts to calibrate quality early and continuously raise the signal bar.
• Iterate rapidly on evaluations, datasets, and feedback loops to improve system performance.
• Act as a quality gate: block claims, pause work, or force scope changes when signal strength or data integrity is insufficient.
• Partner with cross-functional and client-facing teams to translate research progress into clear, credible narratives grounded in evidence.
• Identify gaps in data or evaluation coverage and recommend where to invest, iterate, or stop based on learnings and impact.

What We’re Looking For

• Strong judgment around research signal quality and when work is (or is not) ready to be externalized.
• Experience designing ML-oriented datasets, evaluation frameworks, and QA processes.
• Ability to translate messy, real-world system behavior into structured research and evaluation opportunities.
• Comfort operating in ambiguity, with a bias toward ownership and decisive action.
• Clear written and verbal communication, especially when explaining tradeoffs, limitations, and signal strength to technical and non-technical stakeholders.
• Proven ability to work directly with experts during project kickoff, calibration, and iteration.
• A systems-level mindset, with interest in improving end-to-end model or agent performance rather than isolated components.
• Preferred Experience with reinforcement learning environments, simulators, or feedback-driven training systems.
• Experience improving agentic systems or AI systems operating in real-world workflows.
• Prior work embedded in applied research or production environments with direct impact on deployed systems.
• Experience with evaluation design for complex or real-world tasks.
• Familiarity with expert incentive design and engagement in high-stakes technical projects.

Compensation & Benefits Notice

The national pay range for this full-time position is base salary of $180,000 –$320,000. 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.