Member of Technical Staff, Enterprise AI
Listing checked September 15, 2026 · pay as published by Micro1
Overview
This full-time remote role places you as a forward-deployed research partner inside enterprise AI systems. You will work on live workflows, identify real-world failure modes, and run rapid experiments to improve system performance. The position requires strong judgment in research signal quality, ML-oriented data design, and the ability to translate operational issues into structured research problems. You will collaborate with domain experts and client teams, and contribute to internal and external research artifacts. Compensation is $100 to $130 per hour.
What You'll Do8
- 1Embed within enterprise AI workflows as a research collaborator, working alongside domain experts and client teams.
- 2Surface, formalize, and prioritize system failure modes in real-world deployments.
- 3Design high-signal datasets and evaluation protocols to target identified weaknesses.
- 4Run tight experimental loops to validate hypotheses and quantify improvements.
- 5Produce clear, decision-oriented analyses of system behavior and performance.
- 6Develop and benchmark agentic workflows, focusing on robustness and scalability.
- 7Build lightweight tooling to support evaluation, data curation, and rapid iteration.
- 8Contribute to internal and external research artifacts, including reports and benchmarks.
Requirements8
- 1Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field.
- 2Strong judgment for research signal quality, including data selection and evaluation design.
- 3Experience designing datasets and evaluation frameworks for ML systems.
- 4Ability to translate ambiguous operational issues into structured research problems.
- 5Familiarity with RL environments and/or agentic system evaluation.
- 6Clear, concise communicator with a bias toward actionable insight.
- 7Proven ability to execute in fast iteration cycles and high-ambiguity settings.
- 8Collaborative mindset with experience working across research, product, and domain teams.
Who Should Apply
This role suits someone with a Master’s in CS, ML, AI, or a related field who has designed datasets and evaluation frameworks for ML systems, and who can turn messy operational problems into clear research questions. You should be comfortable with RL environments or agentic system evaluation, and you thrive in fast, ambiguous settings. The role is less suitable for those who prefer well-defined tasks or slow, predictable timelines. Candidates often get rejected when they lack hands-on experience with evaluation design or cannot show how they translated operational issues into research. Another common reason for low fit scores is weak communication of technical insights to non-research stakeholders.
Salary Insight
The pay range for this full-time position is $100 to $130 per hour. The national base salary range is $200,000 to $250,000 USD. Employees are eligible for equity compensation and may receive performance-based bonuses. Benefits include up to 100% reimbursement for health-insurance premiums, paid time off, a 401(K) plan with a company match, and additional benefits for a remote-first workforce.
Pay and demand for Machine Learning & AI roles
AggregatedTypical pay
$75/hour
This role
$100–$130/hr
Most Machine Learning & AI roles pay $55–$100 per hour. This role's pay sits above that range.
Based on 470 similar roles that publish pay · 94 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
- 557
- Listed in last 30 days
- 361
- Remote
- 94%
Hiring most right now: micro1 (218) · Mercor (82) · SME Careers (66)
Most requested skills · share of roles
- python19%
- technical writing9%
- ai evaluation8%
- ai training8%
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Compensation
$100–130/hr
Required Skills
Application Tip
Highlight specific projects where you designed evaluation protocols or datasets for ML systems, and quantify the impact, such as improved accuracy or reduced failure rates. Mention any experience with RL environments or agentic workflows, and show how you turned ambiguous operational issues into structured research problems.
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