TuringCoding & software eval
Senior AI Engineer, Data Quality & Pipeline Automation
Pay not published
About this role, in Turing's words
We are looking for an engineer to build and deploy an intelligent, autonomous operations agent that supervises our data pipeline infrastructure around the clock. The agent will predict delivery delays before they occur, verify data quality using adaptive statistical baselines, and synthesize code-level fixes for routine operational failures. You will own the agent end to end, from design and evaluation to production reliability.
What you'll do
- Predictive health and SLA forecasting: Build models that analyze in-flight pipeline progress, execution velocity, and resource consumption against historical benchmarks to forecast SLA breaches 1-2 hours ahead, triggering priority re-queueing or upstream escalation.
- Bottleneck profiling and self-healing: Enable the agent to inspect execution DAGs and query profiles when jobs stall or degrade, isolate root causes, and autonomously create isolated branches that synthesize fixes for syntax errors, upstream schema changes, and column renames. Each fix is verified through dry-run assertions and submitted as a review-ready change request with full test results for human approval.
- Statistical and semantic data quality forensics: Replace static thresholds with rolling time-series baselines that account for seasonality, day-of-week swings, and business close cycles, and profile categorical entropy, column distributions, and foreign key orphan rates to catch subtle corruption that row counts and schemas miss.
Show all 5 itemsHide the other 2 in What you'll do
- Lineage reconciliation and quarantine: Trace datasets from raw ingestion through marts and reporting views, run cross-tier checksums and consistency assertions before reporting cycles begin, and automatically quarantine partitions with severe violations so bad metrics never reach executive dashboards.
- Conversational copilot and incident management: Generate root-cause incident briefs covering failure cause, affected downstream assets, and recovery steps to cut alert noise, and build a natural language interface for checking pipeline health, triggering selective partition re-runs, and analyzing data distributions.
Candidate background
What this role asks for
- Python
- SQL
- Data Engineering
- Data Management
- Data Governance
Read from the posting's own words: show the exact sentences
- Python: “listed by the platform under required skills: Python”
- SQL: “listed by the platform under required skills: SQL”
- Data Engineering: “listed by the platform under required skills: Data Engineering”
- Data Management: “listed by the platform under required skills: Data Management”
- Data Governance: “listed by the platform under required skills: Data Governance”
Quoted from selected sections of Turing's posting. The full posting may state other conditions. Read the full posting
A closer look
More context, when you need it.
Full job descriptionThe original posting, in Turing's words
Posted by Turing, reproduced here so you can judge the role before clicking. Original posting ↗
About Turing
Turing is one of the world’s leading AGI infrastructure companies, working with frontier AI labs to accelerate model development through high-quality training data, evaluations, and engineering talent.
Engagement Details
• Compensation: Market, please provide a specific hourly rate expectation
• Availability: 40 hours/week, with at least 6 hours of IST/day
• Type: Independent contractor
• Duration: Approximately 2 months
• Start: 01-Nov-2026
About the Role
We are looking for an engineer to build and deploy an intelligent, autonomous operations agent that supervises our data pipeline infrastructure around the clock. The agent will predict delivery delays before they occur, verify data quality using adaptive statistical baselines, and synthesize code-level fixes for routine operational failures. You will own the agent end to end, from design and evaluation to production reliability.
What You Will Do
• Predictive health and SLA forecasting: Build models that analyze in-flight pipeline progress, execution velocity, and resource consumption against historical benchmarks to forecast SLA breaches 1-2 hours ahead, triggering priority re-queueing or upstream escalation.
• Bottleneck profiling and self-healing: Enable the agent to inspect execution DAGs and query profiles when jobs stall or degrade, isolate root causes, and autonomously create isolated branches that synthesize fixes for syntax errors, upstream schema changes, and column renames. Each fix is verified through dry-run assertions and submitted as a review-ready change request with full test results for human approval.
• Statistical and semantic data quality forensics: Replace static thresholds with rolling time-series baselines that account for seasonality, day-of-week swings, and business close cycles, and profile categorical entropy, column distributions, and foreign key orphan rates to catch subtle corruption that row counts and schemas miss.
• Lineage reconciliation and quarantine: Trace datasets from raw ingestion through marts and reporting views, run cross-tier checksums and consistency assertions before reporting cycles begin, and automatically quarantine partitions with severe violations so bad metrics never reach executive dashboards.
• Conversational copilot and incident management: Generate root-cause incident briefs covering failure cause, affected downstream assets, and recovery steps to cut alert noise, and build a natural language interface for checking pipeline health, triggering selective partition re-runs, and analyzing data distributions.
What We Are Looking For
• Python (core): strong production-grade Python for automation, agent logic, testing, and data processing.
• SQL (core): advanced SQL for profiling, validating, and troubleshooting data in warehouses such as Snowflake, BigQuery, or Databricks.
• ETL/ELT and orchestration (core): hands-on experience building and operating pipelines with Airflow (or Dagster/Prefect) and dbt, and understanding of how failures originate and propagate.
• Data quality, observability, and statistics: experience with tools like Great Expectations, Soda, or Monte Carlo, plus a solid grasp of anomaly detection, time-series baselines, and forecasting.
• LLM/agent engineering: experience with tool calling, multi-step agents, and frameworks such as the Claude Agent SDK or LangGraph, with a focus on safety, evaluation, and reliability.
Evaluation Process
• AI interview (~25 minutes)
• Practical code/AI evaluation exercise (~30 minutes)
• Hiring manager interview (~20 minutes)
The practical exercise focuses on your ability to review and evaluate AI-generated code , not competitive programming or algorithm puzzles.
Working with TuringPay, getting started and work, from Turing's own pages
What the platform publishes in general. These are not extra terms confirmed for this particular role.
- Getting paid
- Paid monthly, around the 5th, straight to your bank account, or inside Deel on projects run through Deel. Its help articles disagree on whether that is the 5th calendar day or business day. help.turing.com/articles/9691407757-project-onboarding, help.turing.com/articles/6582454854-what-if-i-haven-t-rec..., help.turing.com/articles/1226770947-project-offboarding
- Before your first paid task
- A short application, with tests on some roles. Turing's review article says an interview is typically required for all roles. Onboarding covers the terms, ID verification with a driver's license or passport, and tax and payment details. help.turing.com/articles/2020987592-apply-for-jobs-at-turing, help.turing.com/articles/9179629201-how-long-does-the-app..., help.turing.com/articles/9691407757-project-onboarding
- How much work
- Review takes from 48 hours to 6 weeks after you finish applying, and the client makes the final choice. No reply within 6 weeks usually means you were not selected. help.turing.com/articles/9179629201-how-long-does-the-app..., help.turing.com/articles/2020987592-apply-for-jobs-at-turing
- Contract
- Depending on the project, the engagement rests on Turing's Terms of Service and a Service Agreement, on Deel, or on a partner that provides the contract. help.turing.com/articles/6582454854-what-if-i-haven-t-rec...
Read on the pages linked above on 23 Sep 2026. Platforms change these terms, so check the linked page before you rely on one.
How does the pay compare?Across Coding & software eval roles, not this role alone
From our own daily reads of every open listing on the platforms we follow, not from Turing's posting. How we count.
Pay across the field: Coding & software eval
Across 394 roles in this field with a stated hourly rate in US dollars, on 14 platforms, the middle half pays $65 to $120 an hour at the top of the range, and the median is $95.
| Platform | Roles | Top of range, per hour |
|---|---|---|
| Alignerr | 137 | $50 to $400 · median $100 |
| micro1 | 46 | $35 to $350 · median $110 |
| Handshake AI | 43 | $47 to $125 · median $80 |
| Meridial (Invisible) | 34 | $22 to $150 · median $60 |
| Mercor | 33 | $60 to $200 · median $105 |
Sources and listing historyWhere this information comes from
This listing's history
- First seen here on 2 Oct 2026, and still on Turing's list when we last read it on 13 Oct 2026.
- Posted by Turing
- First seen by AIPayList
- Last seen live
- Listing ID
- 110234
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