IAM Fullstack Engineer

Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey.

Position: LLM Red Team Specialist — Failure Modes & Edge Cases
Type:Contract
Compensation:$60–$90/hour
Location:Remote
Commitment:35 hours/week

Role Responsibilities

Probe models to explore how frontier AI models handle coding, ML, and analysis tasks. Identify spots where models quietly fail.
Design challenges by turning identified weaknesses into well-crafted tasks that are challenging for models but fair to grade.
Document findings clearly with reproducible evidence and steps.
Strengthen tasks by collaborating with task authors to close loopholes, shortcuts, and grading gaps.
Work as a team by sharing insights with researchers and fellow experts to continually improve the benchmark.

Qualifications

Must-Have

MSc or PhD in a STEM field or equivalent practical experience.
1+ years of experience in research, research-engineering, security, or AI-evaluation roles.
Ability to identify vulnerabilities, edge cases, or failure modes in LLMs or ML systems.
Proficiency in Python and Git for scripting probes and analyses.
Familiarity with LLM capabilities, limitations, and evaluation techniques.
Ability to engage reliably for approximately 35 hours/week.

Preferred

Experience in AI training, model evaluation, or benchmark/task authoring.
High attention to detail, creativity, and strong written communication skills.

Application Process (Takes 20–30 mins to complete)

Upload resume
AI interview based on your resume
Submit form

Resources & Support

For details about the interview process and platform information, please check:
For any help or support, reach out to:

PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.

Originally posted on Himalayas

Job Description

About the job

Position: LLM Red Team Specialist — Failure Modes & Edge Cases
Type:Contract
Compensation:$60–$90/hour
Location:Remote
Commitment:35 hours/week

Role Responsibilities

Probe models to explore how frontier AI models handle coding, ML, and analysis tasks. Identify spots where models quietly fail.
Design challenges by turning identified weaknesses into well-crafted tasks that are challenging for models but fair to grade.
Document findings clearly with reproducible evidence and steps.
Strengthen tasks by collaborating with task authors to close loopholes, shortcuts, and grading gaps.
Work as a team by sharing insights with researchers and fellow experts to continually improve the benchmark.

Qualifications

Must-Have

MSc or PhD in a STEM field or equivalent practical experience.
1+ years of experience in research, research-engineering, security, or AI-evaluation roles.
Ability to identify vulnerabilities, edge cases, or failure modes in LLMs or ML systems.
Proficiency in Python and Git for scripting probes and analyses.
Familiarity with LLM capabilities, limitations, and evaluation techniques.
Ability to engage reliably for approximately 35 hours/week.

Preferred

Experience in AI training, model evaluation, or benchmark/task authoring.
High attention to detail, creativity, and strong written communication skills.

Application Process (Takes 20–30 mins to complete)

Upload resume
AI interview based on your resume
Submit form

Resources & Support

For details about the interview process and platform information, please check:
For any help or support, reach out to:

PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.

Originally posted on Himalayas

Job Description

About the job

Position: LLM Red Team Specialist — Failure Modes & Edge Cases
Type:Contract
Compensation:$60–$90/hour
Location:Remote
Commitment:35 hours/week

Role Responsibilities

Probe models to explore how frontier AI models handle coding, ML, and analysis tasks. Identify spots where models quietly fail.
Design challenges by turning identified weaknesses into well-crafted tasks that are challenging for models but fair to grade.
Document findings clearly with reproducible evidence and steps.
Strengthen tasks by collaborating with task authors to close loopholes, shortcuts, and grading gaps.
Work as a team by sharing insights with researchers and fellow experts to continually improve the benchmark.

Qualifications

Must-Have

MSc or PhD in a STEM field or equivalent practical experience.
1+ years of experience in research, research-engineering, security, or AI-evaluation roles.
Ability to identify vulnerabilities, edge cases, or failure modes in LLMs or ML systems.
Proficiency in Python and Git for scripting probes and analyses.
Familiarity with LLM capabilities, limitations, and evaluation techniques.
Ability to engage reliably for approximately 35 hours/week.

Preferred

Experience in AI training, model evaluation, or benchmark/task authoring.
High attention to detail, creativity, and strong written communication skills.

Application Process (Takes 20–30 mins to complete)

Upload resume
AI interview based on your resume
Submit form

Resources & Support

For details about the interview process and platform information, please check:
For any help or support, reach out to:

PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.

Originally posted on Himalayas

Job Description

About the job

Position: LLM Red Team Specialist — Failure Modes & Edge Cases
Type:Contract
Compensation:$60–$90/hour
Location:Remote
Commitment:35 hours/week

Role Responsibilities

Probe models to explore how frontier AI models handle coding, ML, and analysis tasks. Identify spots where models quietly fail.
Design challenges by turning identified weaknesses into well-crafted tasks that are challenging for models but fair to grade.
Document findings clearly with reproducible evidence and steps.
Strengthen tasks by collaborating with task authors to close loopholes, shortcuts, and grading gaps.
Work as a team by sharing insights with researchers and fellow experts to continually improve the benchmark.

Qualifications

Must-Have

MSc or PhD in a STEM field or equivalent practical experience.
1+ years of experience in research, research-engineering, security, or AI-evaluation roles.
Ability to identify vulnerabilities, edge cases, or failure modes in LLMs or ML systems.
Proficiency in Python and Git for scripting probes and analyses.
Familiarity with LLM capabilities, limitations, and evaluation techniques.
Ability to engage reliably for approximately 35 hours/week.

Preferred

Experience in AI training, model evaluation, or benchmark/task authoring.
High attention to detail, creativity, and strong written communication skills.

Application Process (Takes 20–30 mins to complete)

Upload resume
AI interview based on your resume
Submit form

Resources & Support

For details about the interview process and platform information, please check:
For any help or support, reach out to:

PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.

Originally posted on Himalayas