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Ai Quant Jobs (NOW HIRING)

TIFIN

Lead, Quant AI Investments, TIFIN.ai

Boulder, CO

$150K - $225K/yr

You will translate cutting-edge quantitative research and AI capabilities into scalable, revenue-generating investment products. THE ROLE: * Lead the full lifecycle of quantitative model development ...

TIFIN

Lead, Quant AI Investments, TIFIN.ai

Boulder, CO · On-site

$150K - $225K/yr

Lead the full lifecycle of quantitative model development - from ideation and backtesting to production deployment - across portfolio construction, risk, and factor modeling. * Shape AI-driven ...

Eliassen Group

Quant Developer

Jersey City, NJ · On-site

$80 - $90/hr

Integrate AI models into real-time and batch pipelines. * Optimize analytics and model evaluation for performance, stability, and scalability. * Collaborate with quants, product owners, and ...

OVA.Work

Quantitative AI Analyst

Work is seeking a Quantitative AI Analyst to leverage advanced analytics and artificial intelligence for solving complex business problems. The role involves developing predictive models and ...

DRW

Quantitative AI Strategist

New York, NY · On-site

$132K - $171K/yr

... quant, strategist, or quantitative research role, ideally with exposure to multiple asset classes ... Familiarity with AI technologies and their application to quantitative workflows is a strong plus.

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Ai Quant information

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$98K

$169.7K

$259.5K

How much do ai quant jobs pay per year?

As of Aug 8, 2026, the average yearly pay for ai quant in the United States is $169,729.00, according to ZipRecruiter salary data. Most workers in this role earn between $134,500.00 and $199,000.00 per year, depending on experience, location, and employer.

What is an AI quant?

An AI Quant, or Artificial Intelligence Quantitative Analyst, is a professional who combines expertise in quantitative finance and machine learning to develop advanced trading strategies, risk models, or analytics tools. AI Quants use algorithms, statistical models, and large datasets to identify patterns, forecast market trends, and make data-driven investment decisions. They often work in hedge funds, investment banks, or proprietary trading firms, collaborating with software engineers and other quants to implement and optimize AI-driven financial models.

How does an AI quant typically collaborate with data scientists, traders, and software engineers within a financial institution?

AI Quants often work closely with data scientists to develop and refine machine learning models using financial data, ensuring models are statistically robust and actionable. They collaborate with traders to translate complex quantitative signals into trading strategies that are practical and aligned with market objectives. Additionally, AI Quants partner with software engineers to implement and optimize these models for real-time deployment, ensuring that the underlying code is scalable, efficient, and reliable. This cross-functional environment requires strong communication skills and adaptability, as priorities can shift with market movements and technological advancements.

What are the key skills and qualifications needed to thrive as an AI quant, and why are they important?

To thrive as an AI Quant, you need a strong background in quantitative analysis, mathematics, statistics, and machine learning, often supported by an advanced degree in a quantitative field. Proficiency in programming languages like Python or C++, experience with data analysis libraries (such as NumPy, pandas, and TensorFlow), and familiarity with financial modeling tools are typically required. Strong problem-solving skills, attention to detail, and effective communication set top performers apart in this role. These skills are crucial for developing robust AI-driven trading strategies and ensuring accurate, data-driven decision-making in the fast-paced financial sector.

What is the difference between Ai Quant vs Data Scientist?

AspectAi QuantData Scientist
Required CredentialsAdvanced degrees in quantitative fields, certifications in machine learning or AIDegrees in computer science, statistics, or related fields; certifications vary
Work EnvironmentFinancial firms, hedge funds, or trading firms focusing on quantitative analysisTech companies, research labs, or any industry leveraging data analysis
Employer & Industry UsagePrimarily finance and trading industriesBroad across tech, healthcare, retail, and more
Common Search & Comparison IntentUnderstanding specialized quantitative roles in financeExploring data analysis careers across industries

Ai Quants focus on developing algorithms and models for financial markets, often requiring advanced quantitative skills and finance-specific knowledge. Data Scientists have a broader scope, applying statistical and machine learning techniques across various industries. While both roles involve data analysis and programming, Ai Quants are specialized in finance, whereas Data Scientists work in diverse sectors.

More about Ai Quant jobs
What cities are hiring for Ai Quant jobs? Cities with the most Ai Quant job openings:
What states have the most Ai Quant jobs? States with the most job openings for Ai Quant jobs include:
What job categories do people searching Ai Quant jobs look for? The top searched job categories for Ai Quant jobs are:
Ai Quant jobs near you
Infographic showing various Ai Quant job openings in the United States as of August 2026, with employment types broken down into 74% Full Time, 22% Part Time, and 4% Contract. Highlights an 66% Physical, 3% Hybrid, and 31% Remote job distribution, with an average salary of $169,729 per year, or $81.6 per hour.

Senior AI Quant - eFinancialCareers

eFinancialCareers

Manhattan, NY • On-site

Full-time

Re-posted 9 days ago

Job description

About Us

We are a multi-strategy hedge fund without a platform safety net. No internal capital allocations to compete for. No bureaucracy to hide behind. No “innovation theater.”

We deploy real capital, take real risk, and expect real results.

Our edge is talent density, speed of thought, and the ruthless compounding of small informational advantages. We are building the next layer of that edge in AI.

That’s where you come in.

The Role

You are not here to “apply AI to finance.”

You are here to weaponize machine intelligence across discretionary, systematic, and hybrid strategies — turning fragmented data, messy signals, and human intuition into durable alpha.

You will:

  • Build and deploy production-grade AI/ML models that survive contact with live markets.
  • Extract signal from unconventional, structured, semi-structured, and unstructured data.
  • Partner directly with PMs across asset classes (equities, credit, macro, derivatives, etc.).
  • Design research pipelines that move from idea → backtest → stress test → live capital with minimal friction.
  • Help define what AI-native investing looks like inside a non-platform structure.

You will not be:

  • Tuning Kaggle models.
  • Writing slide decks about “AI transformation.”
  • Shipping notebooks that die in research purgatory.
What Makes This Different
  • Non-platform economics: You are building long-term firm equity value, not renting capital on annual resets.
  • Multi-strategy exposure: Your models won’t live in a silo. They’ll cross-pollinate across books.
  • High agency: If you’re right, capital scales. Quickly.
  • Direct line to decision-makers: No five-layer research committees.
What We’re Actually Looking For

You probably have:

  • 7–15+ years in quant research, AI, ML, or systematic trading.
  • A history of shipping live models with meaningful P&L impact.
  • Deep fluency in Python and modern ML stacks (PyTorch/JAX, distributed training, data engineering).
  • Strong statistical foundations (time series, Bayesian methods, causal inference, optimization).
  • Experience working with alternative data and messy real-world signals.
  • Comfort operating without a roadmap.

Bonus points if you:

  • Have built large-scale training pipelines or inference systems in production.
  • Understand market microstructure and execution.
  • Have blended discretionary and systematic workflows.
  • Have broken something in production and fixed it at 3am.
How We Measure You
  • Signal-to-noise ratio of your ideas.
  • Speed from hypothesis to capital deployment.
  • Sharpe of live models (net of fantasy).
  • Your ability to make PMs smarter.
  • The number of decisions improved by your work.

Not measured:

  • Number of conference talks.
  • GitHub stars.
  • Fancy degrees (though we like smart people).
What You’ll Get
  • Access to deep data, infrastructure, and PM intuition.
  • A seat at the table when strategy evolves.

Compensation is designed for adults:

  • Competitive base.
  • Meaningful performance-linked upside.
  • Long-term economics for those who compound.
Who Thrives Here
  • Builders who care about P&L more than publications.
  • Engineers who think like investors.
  • Investors who think like engineers.
  • People comfortable being uncomfortable.
  • Those who believe AI is not a feature — it’s a new layer of edge.

If you want stability, process, and guardrails — this is not your role.

If you want to help build an AI-native investing engine inside a multi-strategy, non-platform hedge fund — let’s talk.

Apply