Pipeline run

b8200f4f-1a3d-4da3-8a84-d285b6e0680c

Pipeline LLM cost (USD)

API 1: $0.0034 API 2: $0.0000 API 3: $0.0000 Total: $0.0034

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description

Nature of work · ML Infrastructure / MLOps / ML Systems

Create and review ML infra/MLOps training tasks and reference answers, especially around distributed training, training pipelines, and GPU kernel optimization, while giving written feedback and rubrics to align training data quality.

""Design challenging tasks across MLOps, ML infrastructure, and ML systems for use in model training""

Tech stack maturity

AI-Native & Bleeding-Edge

The stack centers on cutting-edge ML systems work like distributed training, GPU kernel optimization, JAX, Pallas, Triton, and framework internals, which is characteristic of AI-native bleeding-edge engineering.

AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)

3.20 / 5

✓ Title match

✓ Has AI skill

✓ AI skill (primary)

· AI skill (secondary)

· On AI team

· Builds AI products

vocab breakdown (legacy)

Assistants (×1): —

Frameworks (×2): —

Models / concepts (×3): LLMs, MLOps, AI, ML, GenAI, Machine Learning

Evidence — skills matched in JD (13)

MLOps Machine Learning Distributed Training Training Pipelines GPU Kernel Optimization ML Infrastructure ML Systems JAX PyTorch Pallas Triton ML Framework Internals Distributed Systems

Skill cluster (5 dimension groups, role-scoped)

ML Frameworks and Libraries

JAX PyTorch

AI Governance and Model Security

Machine Learning

Cloud Platforms

Distributed Systems

Deployment Rollouts and Release Control

MLOps

Cross-cutting / unaligned

Distributed Training Training Pipelines GPU Kernel Optimization ML Infrastructure ML Systems Pallas Triton ML Framework Internals

Show KRA description ↓

Design challenging tasks across MLOps, ML infrastructure, and ML systems for use in model training Write accurate, well-structured reference solutions covering distributed training, training pipelines, and GPU kernel optimization Evaluate submissions from other engineers and provide clear, written technical feedback Develop rubrics and guidelines for assessing training pipeline design, distributed systems reasoning, and kernel-level optimization Collaborate with other ML subject matter experts to maintain consistency and accuracy across the training data Guide research and engineering teams toward closing specific knowledge gaps in ML framework internals 2+ years of professional experience in ML Infrastructure, MLOps, or ML Systems Engineering at a recognized, top-tier organization Hands-on production experience with JAX and/or Py Torch at scale — real training workloads, not coursework or hobby projects Experience writing or optimizing custom GPU kernels with Pallas (JAX) or Triton Demonstrable career progression Strong written English — you can explain complex technical decisions clearly Reliable availability for at least 30 hours/week on weekdays

Signals

Skill ml-engineer

0.62

Alias ml-ops-engineer

1.00

KRA ml-ops-engineer

0.48

Post-classification

Centroidupdated · n=3

Alias collision log—

New-role queue—

New skills captured0

New KRA captured—

Status: extract_from_jd_done Created: 2026-05-19T18:00:56.787165Z Updated: 2026-05-19T18:00:57.575231Z

Flow Current 3-step pipeline

1 POST /skills/extract-from-jd

2 POST /skills/extract-details

3 POST /skills/final-role-output

Role Chosen role & resolution

No chosen role stored for this run.

Job description

Hiring MLOps Engineers (JAX, Py Torch, Pallas/Triton) to help train the next generation of large language models at a leading frontier AI lab.

Remote from India.

$35–$45/hour USD, W-2 contract, 30–40 hrs/week weekdays.

Para AI Labs is sourcing this role on behalf of Mercor — a well-established AI hiring platform — for a leading frontier AI lab's Gen AI team.

You won't be labeling data.

You'll be designing the technical problems, writing reference solutions, and building the rubrics that feed directly into how the next generation of LLMs reason about MLOps and ML systems.

The essentials Pay: $35–$45/hour USD (strong rates for India-based MLOps work) Work mode: Fully remote from anywhere in India Commitment: 30–40 hrs/week, weekdays only Employment: W-2 via Cincinnatus LLC (the employer of record) Placement: A leading frontier AI lab's Gen AI team What you'll do Design challenging tasks across MLOps, ML infrastructure, and ML systems for use in model training Write accurate, well-structured reference solutions covering distributed training, training pipelines, and GPU kernel optimization Evaluate submissions from other engineers and provide clear, written technical feedback Develop rubrics and guidelines for assessing training pipeline design, distributed systems reasoning, and kernel-level optimization Collaborate with other ML subject matter experts to maintain consistency and accuracy across the training data Guide research and engineering teams toward closing specific knowledge gaps in ML framework internals What you need 2+ years of professional experience in ML Infrastructure, MLOps, or ML Systems Engineering at a recognized, top-tier organization Hands-on production experience with JAX and/or Py Torch at scale — real training workloads, not coursework or hobby projects Experience writing or optimizing custom GPU kernels with Pallas (JAX) or Triton Demonstrable career progression Strong written English — you can explain complex technical decisions clearly Reliable availability for at least 30 hours/week on weekdays Why this is worth your time Top-tier USD rates for remote India-based MLOps work Direct, measurable impact on a frontier AI lab's next-generation models Proper W-2 employment via Cincinnatus — no invoice chasing, no contractor tax surprises, no weekend work Work alongside senior ML engineers and researchers from across the global AI ecosystem About Para AI Labs Para AI Labs is the curated destination for high-quality AI training and evaluation roles from leading platforms including Mercor, Scale AI, Turing, and others.

Skills from this JD

Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.

MLOps Primary No API 2 row (run stopped after API 1 or history missing)

Machine Learning Primary No API 2 row (run stopped after API 1 or history missing)

Distributed Training Primary No API 2 row (run stopped after API 1 or history missing)

Training Pipelines Primary No API 2 row (run stopped after API 1 or history missing)

GPU Kernel Optimization Primary No API 2 row (run stopped after API 1 or history missing)

ML Infrastructure Primary No API 2 row (run stopped after API 1 or history missing)

ML Systems Primary No API 2 row (run stopped after API 1 or history missing)

JAX Primary No API 2 row (run stopped after API 1 or history missing)

PyTorch Primary No API 2 row (run stopped after API 1 or history missing)

Pallas Primary No API 2 row (run stopped after API 1 or history missing)

Triton Primary No API 2 row (run stopped after API 1 or history missing)

ML Framework Internals Primary No API 2 row (run stopped after API 1 or history missing)

Distributed Systems Primary No API 2 row (run stopped after API 1 or history missing)

Library artifacts (this run)

No artifact rows for this run.

nano JD Parser — gpt-4.1-nano click to toggle

RoleMLOps Engineer

CompanyMercor

Experience2+ years of professional experience

CTC{'max': 45, 'min': 35, 'raw': '$35–$45/hour USD', 'period': 'hourly', 'currency': 'USD'}

DomainIT Services & Consulting

Location India (remote)

JD type pass

Show raw JSON

{
  "JD_type": "pass",
  "about_company": {
    "source_marker": {
      "first_5_words": "Para AI Labs is the",
      "last_5_words": "including Mercor, Scale AI,"
    },
    "text": "Para AI Labs is the curated destination for high-quality AI training and evaluation roles from leading platforms including Mercor, Scale AI, Turing, and others.",
    "word_count": 27
  },
  "certifications": [],
  "company_name": "Mercor",
  "ctc": {
    "currency": "USD",
    "max": 45,
    "min": 35,
    "period": "hourly",
    "raw": "$35\u2013$45/hour USD"
  },
  "domain": {
    "primary": {
      "aliases": [],
      "domain": "IT Services \u0026 Consulting"
    },
    "secondary": null
  },
  "education": [],
  "experience": {
    "max": null,
    "min": 2,
    "raw": "2+ years of professional experience"
  },
  "job_locations": [
    {
      "aliases": [],
      "city": null,
      "country": "India",
      "state": null,
      "work_mode": "remote"
    }
  ],
  "role": "MLOps Engineer",
  "role_aliases": [
    "MLOps Engineer",
    "Machine Learning Operations Engineer"
  ],
  "role_archetype": "Engineering",
  "roles_and_responsibilities": [
    {
      "bullet_count": 6,
      "heading": "What you\u0027ll do",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Design challenging tasks across MLOps,",
        "last_5_words": "gaps in ML framework internals"
      },
      "text": "Design challenging tasks across MLOps, ML infrastructure, and ML systems for use in model training\nWrite accurate, well-structured reference solutions covering distributed training, training pipelines, and GPU kernel optimization\nEvaluate submissions from other engineers and provide clear, written technical feedback\nDevelop rubrics and guidelines for assessing training pipeline design, distributed systems reasoning, and kernel-level optimization\nCollaborate with other ML subject matter experts to maintain consistency and accuracy across the training data\nGuide research and engineering teams toward closing specific knowledge gaps in ML framework internals",
      "word_count": 66
    },
    {
      "bullet_count": 6,
      "heading": "What you need",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "2+ years of professional experience",
        "last_5_words": "on weekdays"
      },
      "text": "2+ years of professional experience in ML Infrastructure, MLOps, or ML Systems Engineering at a recognized, top-tier organization\nHands-on production experience with JAX and/or Py Torch at scale \u2014 real training workloads, not coursework or hobby projects\nExperience writing or optimizing custom GPU kernels with Pallas (JAX) or Triton\nDemonstrable career progression\nStrong written English \u2014 you can explain complex technical decisions clearly\nReliable availability for at least 30 hours/week on weekdays",
      "word_count": 66
    }
  ],
  "urls": []
}

API 1 — extract-from-jd click to toggle

{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "MLOps"
    },
    {
      "is_primary": true,
      "skill_name": "Machine Learning"
    },
    {
      "is_primary": true,
      "skill_name": "Distributed Training"
    },
    {
      "is_primary": true,
      "skill_name": "Training Pipelines"
    },
    {
      "is_primary": true,
      "skill_name": "GPU Kernel Optimization"
    },
    {
      "is_primary": true,
      "skill_name": "ML Infrastructure"
    },
    {
      "is_primary": true,
      "skill_name": "ML Systems"
    },
    {
      "is_primary": true,
      "skill_name": "JAX"
    },
    {
      "is_primary": true,
      "skill_name": "PyTorch"
    },
    {
      "is_primary": true,
      "skill_name": "Pallas"
    },
    {
      "is_primary": true,
      "skill_name": "Triton"
    },
    {
      "is_primary": true,
      "skill_name": "ML Framework Internals"
    },
    {
      "is_primary": true,
      "skill_name": "Distributed Systems"
    }
  ],
  "jd_role": {
    "display_name": "MLOps Engineer",
    "rationale": null,
    "role_aliases": [
      "MLOps Engineer",
      "Machine Learning Operations Engineer"
    ],
    "role_archetype": "Engineering",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": {
      "source_marker": {
        "first_5_words": "Para AI Labs is the",
        "last_5_words": "including Mercor, Scale AI,"
      },
      "text": "Para AI Labs is the curated destination for high-quality AI training and evaluation roles from leading platforms including Mercor, Scale AI, Turing, and others.",
      "word_count": 27
    },
    "certifications": [],
    "company_name": "Mercor",
    "ctc": {
      "currency": "USD",
      "max": 45,
      "min": 35,
      "period": "hourly",
      "raw": "$35\u2013$45/hour USD"
    },
    "domain": {
      "primary": {
        "aliases": [],
        "domain": "IT Services \u0026 Consulting"
      },
      "secondary": null
    },
    "education": [],
    "experience": {
      "max": null,
      "min": 2,
      "raw": "2+ years of professional experience"
    },
    "job_locations": [
      {
        "aliases": [],
        "city": null,
        "country": "India",
        "state": null,
        "work_mode": "remote"
      }
    ],
    "role": "MLOps Engineer",
    "role_aliases": [
      "MLOps Engineer",
      "Machine Learning Operations Engineer"
    ],
    "role_archetype": "Engineering",
    "roles_and_responsibilities": [
      {
        "bullet_count": 6,
        "heading": "What you\u0027ll do",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "Design challenging tasks across MLOps,",
          "last_5_words": "gaps in ML framework internals"
        },
        "text": "Design challenging tasks across MLOps, ML infrastructure, and ML systems for use in model training\nWrite accurate, well-structured reference solutions covering distributed training, training pipelines, and GPU kernel optimization\nEvaluate submissions from other engineers and provide clear, written technical feedback\nDevelop rubrics and guidelines for assessing training pipeline design, distributed systems reasoning, and kernel-level optimization\nCollaborate with other ML subject matter experts to maintain consistency and accuracy across the training data\nGuide research and engineering teams toward closing specific knowledge gaps in ML framework internals",
        "word_count": 66
      },
      {
        "bullet_count": 6,
        "heading": "What you need",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "2+ years of professional experience",
          "last_5_words": "on weekdays"
        },
        "text": "2+ years of professional experience in ML Infrastructure, MLOps, or ML Systems Engineering at a recognized, top-tier organization\nHands-on production experience with JAX and/or Py Torch at scale \u2014 real training workloads, not coursework or hobby projects\nExperience writing or optimizing custom GPU kernels with Pallas (JAX) or Triton\nDemonstrable career progression\nStrong written English \u2014 you can explain complex technical decisions clearly\nReliable availability for at least 30 hours/week on weekdays",
        "word_count": 66
      }
    ],
    "urls": []
  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "b8200f4f-1a3d-4da3-8a84-d285b6e0680c",
  "stage3_signals": {
    "alias_found": true,
    "alias_match_roles": [
      {
        "display_name": "ML Ops Engineer",
        "matched_count": null,
        "role_id": 16,
        "score": 1.0,
        "slug": "ml-ops-engineer",
        "total_count": null
      }
    ],
    "kra_match_roles": [
      {
        "display_name": "ML Ops Engineer",
        "matched_count": null,
        "role_id": 16,
        "score": 0.4844,
        "slug": "ml-ops-engineer",
        "total_count": null
      },
      {
        "display_name": "ML Engineer",
        "matched_count": null,
        "role_id": 3,
        "score": 0.4771,
        "slug": "ml-engineer",
        "total_count": null
      },
      {
        "display_name": "DevOps Engineer",
        "matched_count": null,
        "role_id": 10,
        "score": 0.3801,
        "slug": "devops-engineer",
        "total_count": null
      },
      {
        "display_name": "Android Engineer",
        "matched_count": null,
        "role_id": 4,
        "score": 0.3657,
        "slug": "android-engineer",
        "total_count": null
      },
      {
        "display_name": "Backend Engineer",
        "matched_count": null,
        "role_id": 1,
        "score": 0.3565,
        "slug": "backend-engineer",
        "total_count": null
      }
    ],
    "skill_match_roles": [
      {
        "display_name": "ML Engineer",
        "matched_count": 8,
        "role_id": 3,
        "score": 0.6154,
        "slug": "ml-engineer",
        "total_count": 13
      },
      {
        "display_name": "ML Ops Engineer",
        "matched_count": 5,
        "role_id": 16,
        "score": 0.3846,
        "slug": "ml-ops-engineer",
        "total_count": 13
      },
      {
        "display_name": "DevOps Engineer",
        "matched_count": 2,
        "role_id": 10,
        "score": 0.1538,
        "slug": "devops-engineer",
        "total_count": 13
      },
      {
        "display_name": "Data Engineer",
        "matched_count": 2,
        "role_id": 2,
        "score": 0.1538,
        "slug": "data-engineer",
        "total_count": 13
      },
      {
        "display_name": "AI Engineer",
        "matched_count": 2,
        "role_id": 13,
        "score": 0.1538,
        "slug": "ai-engineer",
        "total_count": 13
      }
    ]
  },
  "stage4_decision": {
    "alias_collision_detected": false,
    "case": "A",
    "chosen_role": {
      "display_name": "ML Ops Engineer",
      "matched_count": null,
      "role_id": 16,
      "score": 1.0,
      "slug": "ml-ops-engineer",
      "total_count": null
    },
    "confidence": 0.4844,
    "llm2_fired": false,
    "llm2_reasoning": null,
    "queued": false,
    "reasoning": "Stage 1 title \u0027ML Ops Engineer\u0027 (alias match, sim 1.00); KRA agrees (0.48)"
  },
  "stage5_updates": {
    "centroid_n_after": 3,
    "centroid_updated": true,
    "collision_log_id": null,
    "new_kra_attached": null,
    "new_skills_attached": [],
    "queue_entry_id": null,
    "v3_pipeline_triggered": false,
    "v3_role_slug": null,
    "v3_run_id": null
  }
}

API 2 — extract-details

{}

API 3 — final-role-output

{}

LLM Calls

Every model call made for this run, in pipeline order. Click a card to see the model's response.

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