Member of Technical Staff, Enterprise AI
Listing checked August 8, 2026 · pay as published by micro1
Overview
This remote role puts you directly inside live enterprise AI deployments as a hands-on research partner. You'll spot where systems fail in real-world use, design targeted experiments, and run tight iteration cycles to make measurable improvements. It's a mix of research, engineering, and client-facing problem-solving, centered on making AI more robust and scalable in production.
What You'll Do8
- 1Collaborate with domain experts and client teams within active enterprise AI workflows to observe real-world behavior and pinpoint weak spots.
- 2Turn messy operational issues into clearly defined, prioritized research questions and hypotheses.
- 3Build high-signal datasets and evaluation protocols that target specific system weaknesses.
- 4Run short, fast experiments to test hypotheses and quantify whether changes actually improve performance.
- 5Provide clear, decision-ready summaries of system behavior and performance for internal and client stakeholders.
- 6Develop and benchmark agentic workflows, with an emphasis on robustness, scalability, and reliability.
- 7Create lightweight internal tools for evaluation, data curation, and faster iteration.
- 8Contribute to research outputs such as internal reports, public benchmarks, and other shared artifacts.
Requirements8
- 1Master's degree in Computer Science, Machine Learning, AI, or a related technical discipline.
- 2Strong instinct for research signal quality — knowing what data matters and how to evaluate it effectively.
- 3Hands-on experience designing datasets and evaluation frameworks for ML systems.
- 4Ability to convert ambiguous operational problems into structured, testable research problems.
- 5Familiarity with RL environments and/or evaluating agentic systems.
- 6Clear communicator with a bias toward actionable insights, not just analysis.
- 7Comfortable moving quickly in high-ambiguity, fast-iteration environments.
- 8Collaborative track record working across research, product, and domain teams.
Who Should Apply
You thrive when your work is embedded in real systems rather than isolated in a lab. You have a strong sense of what makes data and experiments trustworthy, you communicate with clarity and confidence, and you're comfortable working directly with clients and cross-functional teams. If you enjoy building evaluation tooling, running disciplined experiments, and improving AI reliability in production, this role is for you.
Salary Insight
The full-time base salary for this position is $180,000–$250,000 per year, with eligibility for equity compensation, performance-based bonuses, up to 100% reimbursement for health-insurance premiums, paid time off, and a 401(k) plan with company match.
Pay and demand for AI Training roles
AggregatedTypical pay
$70/hour
This role
$100–$130/hr
Most AI Training roles pay $40–$95 per hour. This role's pay sits above that range.
Based on 1,131 similar roles that publish pay · 302 publish only a top rate; those count at the rate they gave
Rates shown per hour. Yearly and monthly pay converted; one-time fees and non-USD pay are not included.
- Live similar roles
- 1,321
- Listed in last 30 days
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Most requested skills · share of roles
- ai evaluation12%
- llm evaluation12%
- ai training12%
- data annotation9%
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Compensation
$100–130/hr
Required Skills
Application Tip
Don't just list your ML skills — walk through a specific example where you designed an evaluation to solve a real production problem. Explain the dataset you created, the weakness it exposed, and what changed because of it. That kind of story shows exactly how you'll operate in this role.
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