---
title: 'Production Support Engineer - LLM Platform at Qube Research & Technologies'
canonical: 'https://feeny.ai/job/production-support-engineer-llm-platform-qube-research-technologies-new-york-4we9wb8smzkr'
type: 'job'
last_seen: '2026-11-07'
---

# Production Support Engineer - LLM Platform at Qube Research & Technologies

- **Company:** Qube Research & Technologies
- **Location:** New York, NY
- **Posted:** 2026-11-04
- **Last confirmed live:** 2026-11-07
- **Apply:** https://job-boards.greenhouse.io/quberesearchandtechnologies/jobs/8902671002

## Job description

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology and trading expertise has shaped QRT’s collaborative mindset which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors.

You will provide day-to-day production support for QRT’s platform for accessing internally hosted and external LLMs. Based in the US, you will support users during US business hours and coordinate with colleagues in other regions to ensure effective incident handovers.

This is a hands-on support role, focused on incident resolution, user assistance and established operational procedures-not platform architecture, model development or infrastructure engineering.

Your future role within QRT:

- Provide first- and second-line support for QRT’s platform for accessing internally hosted and external LLMs, investigating issues raised by engineering and business users, maintaining support tickets and following issues through to resolution or engineering escalation.
- Monitor platform health using existing dashboards, logs and alerts; identify availability, latency and error-rate problems and perform approved recovery actions using established runbooks.
- Troubleshoot gateway and model-provider issues, including authentication failures, rate limits, timeouts, connection resets, streaming failures and regional performance differences.
- Determine whether incidents originate in a user’s client or configuration, the platform gateway, an internally hosted model or an external LLM provider; collect diagnostic evidence and coordinate escalation to the appropriate team or vendor.
- Support coding assistants, API clients and developer workspaces connected to QRT’s LLM platform, including Claude Code, Codex CLI, OpenCode, PI and Coder; investigate compatibility issues following client or vendor updates.
- Help users configure supported integrations, select the appropriate regional endpoint and migrate API access from Open WebUI to the direct gateway.
- Investigate access and model-availability queries, check user permissions and escalate provisioning or entitlement issues through established support and governance processes.
- Carry out operational checks for releases and configuration changes, including non-production validation, post-release smoke testing and approved rollback procedures, in coordination with platform engineering.
- Use existing observability tools to investigate time to first token, time per output token, percentile latency and failure rates; flag recurring issues and monitoring gaps to the platform team.
- Maintain runbooks, troubleshooting guides and known-issue records; provide clear incident updates and reliable handovers between regional support teams.
- Maintain small support scripts and diagnostic utilities to reduce repetitive checks and improve evidence collection.

Your present skillset:

- Relevant experience in production support, application support or platform operations, with the ability to investigate routine incidents independently and recognise when specialist escalation is required.
- Practical Linux troubleshooting skills and familiarity with Windows-based developer environments.
- Working proficiency in Python, Bash and/or PowerShell for operational checks, log analysis and straightforward support automation.
- Experience using SQL to investigate application issues and query operational records, ideally in PostgreSQL.
- Practical experience interpreting metrics, logs, traces and dashboards to distinguish a platform-wide incident from a user-, client-, model- or region-specific issue.
- Confidence troubleshooting API-driven services, including HTTP errors, authentication, networking, timeouts, retries, rate limiting and streaming connections.
- Hands-on experience supporting LLM-based platforms or services, with a working understanding of LLM APIs, including prompts, context limits, token usage, model identifiers and streaming responses.
- Familiarity with public-cloud services, ideally AWS, and the ability to inspect containerised workloads using established tools and procedures.
- Clear communication with technical and non-technical users, including concise incident updates, realistic expectations and well-documented escalations.
- Awareness of access controls, sensitive-data handling and safe diagnostic practices, including avoiding exposure of prompts, credentials or confidential content in support records.
- A methodical approach to troubleshooting, prioritisation and cross-region handovers

Nice to have:

- Experience supporting AWS Bedrock or other managed model APIs.
- Experience supporting AI coding assistants, API clients or developer workspaces such as Claude Code, Codex CLI, OpenCode, PI or Coder.
- Familiarity with Grafana, Coralogix or equivalent monitoring and log-analysis tools.
- Exposure to LLM gateways or proxies such as LiteLLM, or internally hosted inference services such as vLLM.
- Experience troubleshooting containerised services on ECS or Kubernetes.
- Familiarity with identity and access provisioning, particularly Okta, SCIM and application-level permissions.
- Experience supporting multi-region services and working with globally distributed support and engineering teams.
- Experience coordinating support cases with cloud providers or software vendors during service degradation.

Base salary range for this position is $150,000 to $220,000 per year.

QRT Total Compensation includes discretionary performance-based bonuses and a competitive benefits package.

## About Qube Research & Technologies

## Company Overview
- **One-liner**: Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager that deploys data, research, and technology to execute multi-strategy trading across all asset classes.
- **Entity Type**: Private (Privately Held)
- **Headquarters**: London, United Kingdom
- **Founded**: 2018 (spun off from a systematic trading team at Credit Suisse, with roots tracing to 2016)
- **Founders**: Not publicly disclosed (spun out of Credit Suisse's systematic trading team)

## Core Business
- **Primary industry**: Financial Services / Quantitative Hedge Fund / Systematic Investment Management
- **Target customers**: Institutional investors (pension funds, sovereign wealth funds, endowments, and other large allocators)
- **Mission/purpose**: To combine data, research, technology, and trading expertise to solve complex challenges and deliver high-quality returns for investors.

## Products & Services
- **Multi-Strategy Investment Management**: QRT deploys a diverse range of investment strategies across geographies, asset classes (equities, fixed income, FX, commodities, crypto), and time frames (high-frequency to long-term). They operate as a systematic, data-driven hedge fund.
- **Quantitative Research & Technology Platform**: The firm's core offering is its proprietary technology and research platform, which enables quantitative researchers, data scientists, and software engineers to develop and execute trading models at scale.

## Market Standing
- **Valuation/Market Cap**: Not disclosed (privately held)
- **Key Metric**: Total Funding: Not applicable (privately held, profitable firm). **Assets Under Management (AUM)**: Added $4bn in AUM between the start of 2024 and May 2024, returning 22% from January to April 2024 according to reports.
- **Notable Investors/Partners**: Institutional investors (pension funds, sovereign wealth funds, endowments). Spun off from Credit Suisse's systematic trading team.
- **Growth Signals**: Headcount grew **+56.4% YoY** to 1,787 employees. Expanded to 16 offices across 24 countries (including new offices in Dubai, Chicago, and New York). Over 100 active job openings. AUM grew significantly in 2024.

## Competitive Advantages
- **Data & Technology Moat**: Combines proprietary research, massive datasets, and advanced technology (including machine learning, low-latency C++/FPGA systems, and cloud infrastructure) to generate alpha across all asset classes and timeframes.
- **Multi-Strategy, Multi-Asset Diversification**: Operates across geographies, asset classes, and time horizons, reducing single-strategy risk and allowing capital to flow to the best opportunities.
- **Talent Density & Culture**: Rapidly hires top talent from competitors (Goldman Sachs, Citadel, Millennium, Credit Suisse) and top universities. A young, high-performance culture with 74% of staff aged 40 or under.
- **Compensation Model**: Over 40% of staff are part of a deferred compensation plan with exposure to fund performance, aligning employee incentives directly with investor returns.

## Strategic Focus
- **Aggressive Global Expansion**: Rapidly scaling headcount (+56% YoY) and opening new offices in key financial hubs (Dubai, Chicago, New York, Singapore, Hong Kong).
- **Technology & Infrastructure Investment**: Heavy investment in low-latency trading systems, FPGA engineering, cloud infrastructure, and data platforms (Snowflake, Python, C++, C#).
- **Talent Acquisition**: Prioritizing hiring top quantitative researchers, software engineers, data scientists, and traders from competitors and top universities. Over 100 open roles globally.
- **Crypto & New Asset Classes**: Actively building a crypto trading team and expanding into new asset classes and geographies.

## Why Work Here
- **Compensation & Career Growth**: Highly competitive pay. Interns can earn around $12k per month. Over 40% of staff are in a deferred compensation plan with exposure to fund performance. Glassdoor rating of 4.1/5.0, with compensation (4.0) and career opportunities (4.1) as top highlights.
- **Culture & Demographics**: Young, high-performance culture (74% of staff aged 40 or under, 38% aged 30 or under). 90% of staff reported good team morale in an internal survey. Described as a "fertile ground for experimental research."
- **Work Environment**: Hybrid/office-based depending on role and location. Offices in London (HQ), Paris, Hong Kong, Singapore, New York, Chicago, Dubai, Mumbai, Geneva, and more. New London office in Victoria.
- **Notable Perks**: Deferred compensation plan with fund performance exposure. Interns can earn around $12k per month. Strong focus on work-life balance initiatives.
- **Engineering Culture**: Heavy emphasis on technology (Python, C++, C#, Snowflake, FPGA, low-latency systems, MLflow, Kubernetes). 22% of staff are in Research roles, 6% in Technical roles. Strong support for coding initiatives and academic projects in maths and science.

## Sources
1. [qube-rt.com](https://www.qube-rt.com/)
2. [LinkedIn](https://www.linkedin.com/company/qube-rt)
3. [efinancialcareers-canada.com](https://www.efinancialcareers-canada.com/news/qube-research-and-technologies-hiring-and-culture)
4. [Greenhouse Job Board](http://job-boards.greenhouse.io/quberesearchandtechnologies?page=2)
5. [Careers Page](https://www.qube-rt.com/careers)

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