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Machine Learning Finance Jobs (NOW HIRING)

Virtu Financial

Machine Learning Engineer

New York, NY · On-site

$200K - $300K/yr

Virtu's Research Technology team is looking for an experienced Machine Learning Engineer to join a ... Build and maintain data pipelines that move financial data from storage into training workflows ...

Virtu Financial

Machine Learning Engineer

New York, NY

$200K - $300K/yr

Virtu's Research Technology team is looking for an experienced Machine Learning Engineer to join a ... Build and maintain data pipelines that move financial data from storage into training workflows ...

Jane Street

Machine Learning Engineer

New York, NY

Machine learning is a critical pillar of Jane Street's global business. Our ever-evolving trading ... If you've never thought about a career in finance, you're in good company. Many of us were in the ...

Virtu Financial

Machine Learning Researcher

New York, NY · On-site

$200K - $300K/yr

As a Machine Learning Researcher at Virtu, you'll pursue high-impact research opportunities within ... Adapt techniques from your area of expertise to achieve breakthrough results in the financial ...

Virtu Financial

Machine Learning Researcher

New York, NY

$200K - $300K/yr

As a Machine Learning Researcher at Virtu, you'll pursue high-impact research opportunities within ... Adapt techniques from your area of expertise to achieve breakthrough results in the financial ...

Jane Street

Machine Learning Engineer

New York, NY · On-site

Machine learning is a critical pillar of Jane Street's global business. Our ever-evolving trading ... If you've never thought about a career in finance, you're in good company. Many of us were in the ...

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Machine Learning Finance information

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$25K

$92.6K

$135.5K

How much do machine learning finance jobs pay per year?

As of Jul 25, 2026, the average yearly pay for machine learning finance in the United States is $92,631.00, according to ZipRecruiter salary data. Most workers in this role earn between $75,000.00 and $109,000.00 per year, depending on experience, location, and employer.

What job makes $1,000,000 a year?

In the field of machine learning finance, highly senior roles such as Chief Data Officer or Quantitative Hedge Fund Manager can earn $1,000,000 or more annually, especially with bonuses and profit sharing. These positions typically require advanced degrees, extensive experience, and expertise in algorithms, financial modeling, and programming tools like Python or R.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence or machine learning within finance or technology sectors, often involving advanced skills in data analysis, programming, and model development. Such roles may include AI research scientists, machine learning engineers, or senior data scientists, and usually require extensive experience, specialized certifications, and proficiency with tools like Python, TensorFlow, or cloud platforms.

Can machine learning be used in finance?

Machine learning is widely used in finance for tasks such as risk assessment, fraud detection, algorithmic trading, and portfolio management. Machine learning finance professionals develop models using programming languages like Python and tools such as TensorFlow or scikit-learn to analyze large datasets and improve decision-making processes.

What are the key skills and qualifications needed to thrive in the Machine Learning Finance position, and why are they important?

To excel in Machine Learning Finance, you need strong quantitative skills, proficiency in programming (typically Python or R), and a solid background in both finance and machine learning, often supported by a relevant degree such as in computer science, statistics, mathematics, or finance. Familiarity with machine learning libraries (like TensorFlow, scikit-learn), financial modeling tools, and certifications such as CFA or FRM can be highly beneficial. Excellent problem-solving abilities, communication skills, and a collaborative attitude help professionals translate complex data into practical financial insights and work effectively with both technical and non-technical stakeholders. These competencies enable you to create robust predictive models, drive innovation in financial analysis, and ensure sound decision-making in dynamic industry settings.

What is the salary of ML in finance?

Machine Learning professionals in finance typically earn between $80,000 and $150,000 annually, depending on experience, location, and specific role. Senior roles or those with advanced skills in data analysis, programming, and financial modeling can earn higher salaries, often exceeding $200,000 with bonuses and incentives.

What are some typical challenges faced by professionals in Machine Learning Finance roles?

Professionals in Machine Learning Finance often encounter challenges such as working with noisy or incomplete financial data, keeping up with rapidly evolving algorithms, and ensuring model compliance with industry regulations. They may also need to bridge the gap between technical model development and practical business needs, communicating complex findings to non-technical teams. These roles typically involve close collaboration with traders, financial analysts, and risk managers to ensure that machine learning solutions are both accurate and actionable. Facing these challenges can be rewarding, offering significant opportunities for skill development and career advancement in a data-driven financial landscape.

What is a Machine Learning Finance job?

A Machine Learning Finance job involves applying machine learning techniques to financial problems such as risk assessment, algorithmic trading, fraud detection, and portfolio optimization. Professionals in this field build predictive models, analyze large datasets, and automate decision-making processes to improve financial performance. They typically work with tools like Python, TensorFlow, and financial datasets to develop AI-driven solutions. These roles require expertise in machine learning, statistics, and financial markets, often blending data science with quantitative finance.

More about Machine Learning Finance jobs
What cities are hiring for Machine Learning Finance jobs? Cities with the most Machine Learning Finance job openings:
What are the most commonly searched types of Machine Learning Finance jobs? The most popular types of Machine Learning Finance jobs are:
What states have the most Machine Learning Finance jobs? States with the most job openings for Machine Learning Finance jobs include:
What job categories do people searching Machine Learning Finance jobs look for? The top searched job categories for Machine Learning Finance jobs are:
Machine Learning Finance jobs near you
Infographic showing various Machine Learning Finance job openings in the United States as of July 2026, with employment types broken down into 67% Full Time, and 33% Contract. Highlights an 100% In-person job distribution, with an average salary of $92,631 per year, or $44.5 per hour.
Machine Learning Engineer

Machine Learning Engineer

Virtu Financial

New York, NY • On-site

$200K - $300K/yr

Full-time

Posted 15 days ago

Job description

Virtu's Research Technology team is looking for an experienced Machine Learning Engineer to join a small group of technologists whose primary function is building the infrastructure that powers our quantitative researchers. This is a unique opportunity to work at the intersection of machine learning and systematic trading - building tools that directly determine how fast our researchers can move, and how effectively our GPU cluster translates into research output.
In this role, you will be responsible for the development of our ML research platform: the systems that manage data and compute, track experiments, and enable researchers to go from idea to result as efficiently as possible. You will work closely with quants and engineers alike and will play a central role in shaping how ML is done at the firm as we scale our capabilities. We mostly use Python, C++ and Java with a variety of open-source tools along with proprietary solutions.
THE ROLE
  • Design and build experiment tracking, job orchestration, and reproducibility infrastructure so researchers can iterate quickly, compare runs reliably, and recover from failures without losing work
  • Create tools for all stages of the simulation lifecycle including historical back-tests and production monitoring. Add new features to our simulators
  • Own visibility into GPU cluster utilization - track allocation, surface bottlenecks, and ensure our compute investment is being used effectively
  • Diagnose and resolve performance issues across training pipelines: data loading throughput, storage I/O, GPU utilization, and inter-node communication in distributed training runs
  • Build and maintain data pipelines that move financial data from storage into training workflows efficiently, with strong guarantees on correctness and versioning
  • Develop feature storage and retrieval patterns that support fast, reproducible access to training data at scale
  • Work directly with researchers to understand friction in their workflows, and build solutions that reduce it - from tooling improvements to infrastructure changes
  • Collaborate with existing infrastructure engineers on capacity planning, cloud/on-prem tradeoffs, and tooling decisions - this is a collaborative environment, not a siloed one
  • Stay current with developments in ML infrastructure tooling and bring relevant ideas and tools into our stack where they create genuine value

THE CANDIDATE
  • 5+ years of experience in ML engineering, research infrastructure, or HPC environments
  • Strong Python engineering skills - you write clean, maintainable, well-tested code that other engineers want to build on. Exposure to C++ in a performance-sensitive context is a plus
  • Experience building or operating distributed training infrastructure, with working knowledge of how collective communication libraries (NCCL, Horovod, or similar) behave at scale
  • Practical experience with experiment tracking systems and strong opinions about what good research infrastructure looks like
  • Comfort working across the Linux systems stack - storage, networking, job scheduling - enough to follow a problem wherever it leads
  • Excellent communication skills and the ability to work closely with researchers and engineers across disciplines
  • Intellectually curious and self-driven - you proactively identify problems worth solving, not just problems you've been asked to solve

DESIRED, BUT NOT REQUIRED
  • Experience with on-prem compute environments and job orchestration tools such as Slurm
  • Familiarity with GPU profiling tools (NSight Systems, PyTorch Profiler) and hands-on experience optimizing GPU memory or compute utilization
  • Experience with columnar data formats and high-performance data processing tools such as Parquet, Arrow, and Polars
  • Familiarity with workflow orchestration tools (Prefect, Dagster, or similar)
  • Prior experience in environments with high-stakes, time-series data at scale. Open to Quantitative Finance, Algorithmic Trading, and Other
  • Experience contributing to or extending open-source ML frameworks or infrastructure tooling

Salary Range: $200,000 - $300,000 (salary range is exclusive of bonuses, benefits or other categories of compensation)
Virtu Financial is an equal opportunity employer, committed to a diverse and inclusive workplace, welcoming you for who you are and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.

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