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Member of Technical Staff, Enterprise AI
micro1 is hiring a Member of Technical Staff, Enterprise AI to function as a forward-deployed research partner embedded directly within enterprise AI systems. This full-time role focuses on live enterprise workflows, identifying real-world system failure modes, designing high-signal datasets and evaluation protocols, and running rapid experimental cycles to improve AI system performance. The role combines enterprise AI, machine learning evaluation, agentic workflow development, research engineering, and close collaboration with domain experts and client teams.
Salary
USD200,000 - USD250,000 / year
Department
Enterprise AI & Research
Experience
Experience designing datasets and evaluation frameworks for machine learning systems, translating ambiguous operational issues into structured research problems, and executing rapid experimental cycles in high-ambiguity environments; enterprise AI, client-facing, agentic system evaluation, or forward-deployed research experience is preferred
Curated by Farooq77 Jobs
Role fit snapshot
- Engagement
- Full-time remote | micro1 application platform
- Experience
- Recorded experience: Experience designing datasets and evaluation frameworks for machine learning systems, translating ambiguous operational issues into structured research problems, and executing rapid experimental cycles in high-ambiguity environments; enterprise AI, client-facing, agentic system evaluation, or forward-deployed research experience is preferred
- Core expertise
- Enterprise AI & Research | Enterprise AI | Machine Learning | Artificial Intelligence
- Expected work/output
- Research artifacts
- Geographic eligibility
- Not specified beyond Remote
- Listing dates
- Posted 2026-09-16 | Valid through 2026-10-16
- Compensation
- Recorded compensation: $200,000-$250,000/year
Responsibilities
- Embed within enterprise AI workflows as a research collaborator working alongside domain experts and client teams.
- Identify, formalize, and prioritize system failure modes observed in real-world enterprise AI deployments.
- Design high-signal datasets and evaluation protocols targeting identified model and system weaknesses.
- Run rapid experimental loops to validate research hypotheses and quantify improvements in system performance.
- Produce clear and decision-oriented analyses explaining AI system behavior, performance, and research findings.
- Develop and benchmark agentic workflows with an emphasis on robustness and scalability.
- Build lightweight tooling supporting evaluation, data curation, experimentation, and rapid iteration.
- Translate ambiguous operational challenges into structured and measurable research problems.
- Collaborate across research, product, domain, and client teams to improve deployed AI systems.
- Contribute to internal and external research artifacts, including technical reports, evaluations, and benchmarks.
Skills
Enterprise AIMachine LearningArtificial IntelligenceResearch Signal JudgmentML-Oriented Data DesignOperations-to-Research TranslationDataset DesignEvaluation FrameworksEvaluation ProtocolsAI EvaluationSystem Failure AnalysisFailure Mode AnalysisExperimental DesignRapid ExperimentationHypothesis TestingAgentic WorkflowsAgentic System EvaluationReinforcement Learning EnvironmentsBenchmarkingData CurationEvaluation ToolingResearch ToolingSystem Performance AnalysisEnterprise AI DeploymentsForward-Deployed ResearchTechnical ResearchClient CollaborationCross-Functional CollaborationTechnical Communication
Requirements
- Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field.
- Strong judgment for research signal quality, including effective data selection and evaluation design.
- Experience designing datasets and evaluation frameworks for machine learning systems.
- Ability to translate ambiguous operational issues into clearly structured research problems.
- Familiarity with reinforcement learning environments and/or agentic system evaluation.
- Ability to analyze real-world AI system behavior and identify meaningful failure modes.
- Strong ability to execute rapid experimental cycles and work effectively in high-ambiguity environments.
- Clear and concise written and verbal communication skills with a focus on actionable insights.
- Collaborative mindset with experience working across research, product, and domain teams.
- Strong client-facing experience in technical or research-driven environments is preferred.
- Experience building internal research or evaluation tooling is preferred.
- Contributions to benchmarks, research publications, or open research initiatives are preferred.
- Experience with enterprise AI deployments or forward-deployed research models is preferred.
Benefits
- Full-time remote position.
- Base salary of $200,000–$250,000 per year.
- Eligibility for equity compensation.
- Potential performance-based bonuses subject to role and company policies.
- Up to 100% reimbursement for health insurance premiums.
- Paid time off.
- 401(k) plan with company match.
- Additional benefits supporting a high-performing remote-first workforce.
- Opportunity to work directly with live enterprise AI deployments and real-world workflows.
- Opportunity to develop datasets, evaluations, agentic workflows, and research systems that directly improve enterprise AI performance.
Before you apply
- Confirm that your location is eligible for the role.
- Confirm the employment or contract type.
- Verify the current compensation at the official source.
- Review the required skills and experience.
- Check that the listing is still open before applying.
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