About Company
The intersection of quantitative finance and artificial intelligence is reshaping Wall Street. Mercor, an elite talent infrastructure platform that partners with frontier AI research labs, is hiring Fixed Income Trading Experts. In this specialized role, you will act as a Subject Matter Expert (SME) to train, evaluate, and fine-tune large language models (LLMs) and autonomous agents designed for advanced financial intelligence, market making, and portfolio management.
Job Details
| Role: | Fixed Income Trading Professional |
| Salary: | $150-$180 per hour |
| Location: | United States (Remote) |
| Job Type: | fulltime |
| Posted Date: | 01/06/2026 |
| Application Deadline: | Apply Soon |
Job Description
Artificial Intelligence models are highly proficient at processing text, but they lack the intrinsic market intuition, institutional realism, and execution nuances possessed by seasoned Wall Street traders. As a Fixed Income Trading Professional, your primary mission is to inject human-grade expertise into the training loops of frontier AI models.
Requirements
- Tenure: 3 to 10+ years of active, hands-on experience in fixed income trading, sales & trading, or fixed-income portfolio management.
- Institutional Background: Proven track record working at top-tier tier-1 investment banks, hedge funds, asset management firms, or proprietary trading desks.
- Core Platforms & Workflows:
- Trading Desks: Active, day-to-day experience executing fixed-income instruments.
- Electronic Trading Systems: Deep, hands-on familiarity with institutional execution platforms such as Tradeweb, MarketAxess, or the Bloomberg Terminal (FIT/TSOX functions).
- Market Mechanics: Comprehensive understanding of Request for Quote (RFQ) workflows, automated price discovery engines, liquidity pools, and institutional order management systems (OMS).
- Domain-Specific Expertise (One or More Tracks Required):
- Rates Trading: US Treasuries (On-the-run and Off-the-run), interest rate swaps, curve trades (steepeners/flatteners), interest rate futures, and repo markets.
- Credit Trading: Corporate bonds (Investment Grade – IG, High Yield – HY), credit default swaps (CDS), and distressed debt instruments.
- Structured Products: Mortgage-Backed Securities (MBS, TBA trading), Asset-Backed Securities (ABS), and Collateralized Loan Obligations (CLOs).
- Emerging Markets (EM): Sovereign and corporate debt frameworks in emerging economies.
- Technical & Mathematical Base:
- Bond Math Proficiency: Flawless grasp of fixed-income mathematical foundations:
- Duration: Modified Duration and MacCaulay Duration for price sensitivity.
- Convexity: Second-order price sensitivity calculations:$$\Delta P \approx -D \cdot \Delta y + \frac{1}{2} C \cdot (\Delta y)^2$$
- Risk Metrics: Core risk sensitivity dimensions such as DV01 (Dollar Value of an eighth), carry, and roll-down profiles.
- Analytical Dissection: Ability to break down a complex, multi-leg trade structure into clear, logical, step-by-step reasoning sequences.
- Preferred Skills (Nice to Have):
- Proficiency with quantitative tools, specifically Python (Pandas, NumPy, QuantLib) and advanced Excel financial modeling.
- An active, demonstrated interest in the application of Machine Learning (ML), Reinforcement Learning from Human Feedback (RLHF), and LLMs in quantitative finance.
Roles and Responsibilities
- Model Output Evaluation: Critically review, score, and improve AI-generated insights, research reports, and trading code outputs focusing on yield curves, bond pricing models, and macro-driven strategies.
- Scenario Engineering: Design highly realistic, historical, and synthetic trading scenarios—including extreme market edge cases (e.g., liquidity crunches, sudden rate shocks, basis trade squeezes)—to stress-test AI behavior.
- Reasoning Auditing: Audit the step-by-step logical reasoning paths of AI models regarding trade structuring, hedging strategies, portfolio rebalancing, and risk allocation decisions.
- Dataset Annotation: Author, curate, and annotate high-fidelity financial datasets and multi-turn prompt examples to be used in training frontier models via supervised fine-tuning (SFT).
- Institutional Alignment: Provide candid feedback to AI researchers regarding the realism, market vocabulary, compliance awareness, and intuition of the system’s financial outputs.
- Cross-Functional Syncs: Collaborate closely with world-class AI engineers and research scientists to translate financial trading workflows into machine-interpretable feedback parameters.
How to Apply?
Role Summary
You will evaluate, stress-test, and refine AI-generated outputs dealing with bond pricing, yield curves, and risk management strategies. This is a highly technical, project-based opportunity where you will translate your real-world experience executing transactions into structured feedback and high-quality training datasets. Your insights will directly dictate how next-generation financial AI systems understand and operate within complex, multi-trillion-dollar fixed income ecosystems.
Company Culture & Insights
Working on a contract basis through Mercor for an AI research lab places you inside a highly progressive, fast-paced, and scientifically rigorous ecosystem. This is a “Matrixed Technical Environment” where Wall Street domain expertise meets Silicon Valley computer science.
The culture focuses entirely on data quality and objective validation. The researchers you interact with are experts in model architectures (Transformers, Reinforcement Learning) but rely on you to enforce absolute financial accuracy. This contract offers high autonomy; you work completely remote, manage your own schedule, and apply your specialized market knowledge in a novel environment completely unburdened by legacy banking bureaucracy.
Why We Recommend This Job
- Pioneering Tech Exposure: This is an opportunity to look under the hood of the frontier AI systems that will define institutional trading over the next decade.
- Flexible Income Stream: Because this is an independent contractor role with no fixed hours, you can seamlessly consult and generate significant hourly revenue alongside your existing professional commitments, provided you maintain strict adherence to non-disclosure boundaries.
- Zero Proprietary Conflict: Your work with Mercor focuses entirely on public market data, foundational theories, and generalized scenarios. It does not involve accessing, using, or exposing confidential or proprietary trading strategies from any previous or current employer.
Career Growth Potential
- Engaging as an AI Training Expert opens a highly unique, contemporary career trajectory that bridges finance and technology:
Fixed Income Trader ➔ Financial AI Consultant ➔ Prompt Engineer (Finance) ➔ Quant-AI Alignment Lead ➔ AI Lab Product Director- While traditional finance paths move from Associate to Managing Director, this role builds your profile for the booming Quant-AI and FinTech sectors. Demonstrating the ability to successfully align and train frontier models on bond mathematics makes you an incredibly rare and valuable asset for hedge funds and tech firms building autonomous execution infrastructure.
Skills You’ll Gain
| Category | Specific Technical Competencies |
| AI Alignment | Reinforcement Learning from Human Feedback (RLHF), Supervised Fine-Tuning (SFT) data prep. |
| Prompt Engineering | Structuring complex chain-of-thought (CoT) prompts for multi-leg financial transactions. |
| Risk Modeling | Simulating synthetic extreme tail-risk scenarios and execution bottlenecks for AI testing. |
| Systemic Logic | Deconstructing implicit market intuition into explicit, algorithmic instruction steps. |
Salary & Benefits Info
- Hourly Compensation: Handled on a competitive independent contractor basis. Rates for highly specialized Wall Street talent (3–10+ years experience in fixed income) typically command premium hourly brackets commensurate with institutional consulting rates.
- Payment Infrastructure: Disbursed weekly on a predictable schedule via Stripe or Wise, allowing for efficient cash-flow management.
- Flexibility Perks: Complete ownership of your time. There are no mandatory call hours or rigid structural templates; you scale your contribution based on your weekly availability.
Frequently Asked Interview Questions
- Yield Curve Mechanics: “If an AI model suggests entering a 2s10s yield curve flattener using US Treasuries, walk me through the step-by-step reasoning loop it must generate to calculate the exact duration-neutral ratio. How would you correct the model if it fails to account for roll-down effects?”
- DV01 Risk Allocation: “Explain how you would construct a prompt-response dataset to teach an AI model how to hedge a high-yield corporate bond portfolio using CDX indices. What parameters must be set to ensure the model matches the DV01 risk profile accurately?”
- RFQ Execution Anomalies: “An autonomous AI agent is executing an automated RFQ loop on Tradeweb during a period of high macro volatility (e.g., an unexpected CPI print). What specific edge cases or behavior warnings must we program into its logic to protect it from ‘winner’s curse’ or phantom liquidity?”
- Convexity Analysis: “How do you explain the concept of negative convexity in callable MBS products to an AI model that only understands linear relationships? Write out the reasoning progression you would use to grade the model’s understanding of prepayment risk.”
Behavioral & Culture Questions
- Deconstructing Intuition: “Much of successful trading relies on ‘gut feel’ and implicit market intuition developed over years on a desk. Describe a time you had to break down a fast, intuitive trading decision into a highly detailed, logical, step-by-step explanation for a non-finance professional or system programmer.”
- Managing Algorithmic Contradiction: “Imagine an AI model generates a relative value trading recommendation that is mathematically sound based on historical data but fundamentally ignores current geopolitical realities or institutional market structure. How would you structure your critique to improve the model’s contextual awareness?”
- Detail & Quality Control: “Data quality is paramount for frontier model training. Tell me about your strategy for maintaining high cognitive focus and extreme attention to detail when review-reading or annotating large volumes of highly dense, complex technical financial text.”