Remote Multi-Asset Quant Trader — Cross-Asset Execution

Anemoi Predictive Technology LLC.

Albuquerque (NM)

Remote

USD 190,000 - 285,000

Full time

14 days+
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Benefits offered by this job

Medical benefits
401(k) matching
Flexible PTO
Parental leave
Learning budget
Home-office stipend
Bonus & equity eligibility

Job summary

Anemoi Predictive Technology LLC. is hiring a fully remote Quantitative Trader based in the Albuquerque, NM area to trade and evaluate systematic strategies across liquid equities, ETFs, futures, and FX.

The position operates on Mountain Time and spans early-market and late-session windows. The role emphasizes cross-asset exposure, funding, rolls, execution quality, and risk controls, with collaboration across research, engineering, trading, and risk teams to manage uncertainties and operational

Qualifications

  • Four or more years of systematic multi-asset trading experience.
  • Strong derivatives, statistics, and market-mechanics knowledge.
  • Proficiency in Python or similar quantitative programming.

Responsibilities

  • Manage live exposures and instrument-specific risk limits
  • Review execution quality, funding, rolls, and calendar effects
  • Research cross-asset relationships without forcing unstable correlations
  • Lead incident review and operating-control improvements
  • Own cross-asset exposure, funding and roll events, execution quality, and venue-specific risk during Mountain Time monitoring across both early-market and late-session operating windows.

Skills

Systematic multi-asset trading
Derivatives knowledge
Statistics knowledge
Python programming

Job description

Anemoi Predictive Technology LLC. is hiring a fully remote Quantitative Trader based in the Albuquerque, NM area to trade and evaluate systematic strategies across liquid equities, ETFs, futures, and FX.

The position operates on Mountain Time and spans early-market and late-session windows. The role emphasizes cross-asset exposure, funding, rolls, execution quality, and risk controls, with collaboration across research, engineering, trading, and risk teams to manage uncertainties and operational

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