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Private Equity Machine Learning Jobs (NOW HIRING)

Hang

Machine Learning Engineer

New York, NY · On-site +1

This person will implement and develop machine learning models to enhance our platform ... Competitive salary & equity compensation. * Quarterly company offsites

Hive

Machine Learning Manager

Seattle, WA

$180K - $250K/yr

Machine Learning Manager In order to execute our vision, we're constantly growing our machine ... Hired applicant may receive an equity grant in the form of an option to purchase stock in the ...

Coin Flow

Machine Learning Engineer

Chicago, IL

$160K - $220K/yr

Coinflow is seeking a Machine Learning Engineer to help build the intelligence layer that powers ... In addition, the employee who fills this role will be eligible for an equity grant, allowing you to ...

Spotify

Machine Learning Engineer

Manhattan, NY · On-site +1

$170K - $212K/yr

Machine Learning Engineer The Music Promotion team is building products that allow creators to ... The United States base range for this position is $170,000 - $212,000 plus equity. The benefits ...

Coinflow

Machine Learning Engineer

Chicago, IL · On-site

$160K - $220K/yr

Coinflow is seeking a Machine Learning Engineer to help build the intelligence layer that powers ... In addition, the employee who fills this role will be eligible for an equity grant, allowing you to ...

Hive

Machine Learning Manager

Seattle, WA · On-site

$180K - $250K/yr

Machine Learning Manager In order to execute our vision, we're constantly growing our machine ... Hired applicant may receive an equity grant in the form of an option to purchase stock in the ...

Neuralink

Machine Learning Engineer

Austin, TX · On-site

$199K - $331K/yr

About the Role: Engineers on the BCI team utilize signal processing and machine learning to ... As such, in addition to base salary, Neuralink offers equity compensation (in the form of ...

Hive

Machine Learning Manager

Seattle, WA

$180K - $250K/yr

Machine Learning Manager In order to execute our vision, we're constantly growing our machine ... Hired applicant may receive an equity grant in the form of an option to purchase stock in the ...

Robinhood

Machine Learning Engineer

Bellevue, WA

We're looking for an exceptional Machine Learning Engineer to help shape the future of our core ... Performance driven compensation with multipliers for outsized impact, bonus programs, equity ...

EarnIn

Head of Machine Learning

Mountain View, CA · Hybrid

$477K - $583K/yr

... equity and benefits. Our salary ranges are determined by role, level, and location. This is a ... Define and own the machine learning roadmap in alignment with business goals. * Lead the ML ...

Latent

Machine Learning Engineer

San Francisco, CA · On-site

$225K - $300K/yr

Machine Learning Engineer About Latent Health Healthcare today is only truly personalized for two ... Meaningful equity in an early-stage, Series A company Closing If you're interested in building ...

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All JobsPrivate Equity Machine Learning Jobs

Private Equity Machine Learning information

See salary details

$47K

$100.2K

$143K

How much do private equity machine learning jobs pay per year?

As of Jun 9, 2026, the average yearly pay for private equity machine learning in the United States is $100,180.00, according to ZipRecruiter salary data. Most workers in this role earn between $69,000.00 and $120,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Private Equity Machine Learning professional, and why are they important?

To thrive as a Private Equity Machine Learning professional, you need a strong background in finance, quantitative analysis, and machine learning, typically supported by degrees in finance, computer science, or related fields. Proficiency in programming languages such as Python or R, experience with machine learning libraries (e.g., TensorFlow, scikit-learn), and familiarity with financial modeling tools are essential. Strong problem-solving abilities, attention to detail, and effective communication skills help bridge technical insights with investment strategies. These capabilities are crucial for identifying data-driven investment opportunities, optimizing portfolio performance, and supporting rigorous, evidence-based decision-making.

What is Private Equity Machine Learning?

Private Equity Machine Learning refers to the application of machine learning algorithms and data analytics in the private equity industry. Professionals in this field use advanced data science techniques to analyze large datasets, identify investment opportunities, optimize portfolio management, and improve due diligence processes. By leveraging machine learning, private equity firms can gain deeper insights into market trends, predict company performance, and make more informed investment decisions. This approach helps firms stay competitive in a data-driven financial landscape.

How does a Private Equity Machine Learning professional typically collaborate with investment teams during deal sourcing and due diligence?

In a Private Equity Machine Learning role, you’ll work closely with investment teams by developing and deploying data-driven models to identify attractive investment opportunities and assess potential risks. You may help automate the screening of large datasets to uncover patterns, forecast performance, or flag anomalies that inform deal sourcing. During due diligence, your analyses support valuation, growth projections, and operational insights, often requiring clear communication of technical findings to non-technical colleagues. This collaborative environment allows you to directly influence investment decisions while gaining exposure to both analytical and business aspects of private equity.

Which 3 jobs will survive AI?

Private Equity professionals, especially those involved in deal sourcing, due diligence, and portfolio management, are likely to continue thriving as AI tools assist but do not replace strategic decision-making. Data scientists and machine learning engineers will remain essential for developing and maintaining AI models used in investment analysis. Additionally, compliance officers and legal experts will continue to be vital for navigating regulatory requirements in the evolving financial landscape.

What is the difference between Private Equity Machine Learning vs Data Scientist in Private Equity?

AspectPrivate Equity Machine LearningData Scientist in Private Equity
Required CredentialsDegree in Computer Science, Data Science, or related fields; experience with machine learning frameworksDegree in Statistics, Data Science, or related fields; strong programming skills
Work EnvironmentFocus on developing ML models for investment analysis, often in finance-focused teamsAnalyze data, build models, and generate insights for investment decisions within private equity firms
Employer & Industry UsagePrivate equity firms, hedge funds, financial institutionsPrivate equity firms, investment banks, financial consultancies

While both roles involve data analysis and programming, Private Equity Machine Learning specialists focus on developing advanced algorithms to predict investment outcomes, whereas Data Scientists in Private Equity analyze data to support investment decisions. The roles often overlap but differ in technical focus and application within the private equity industry.

Senior Software Engineer (Machine Learning)

Senior Software Engineer (Machine Learning)

Valor Equity Partners

Chicago, IL • On-site

$126K - $166K/yr

Other

Posted 18 days ago

Job description

Senior Software Engineer (Machine Learning)

Chicago

Valor Equity Partners is a different kind of private investment firm. We pioneered the idea of operational growth. We work side-by-side, shoulder-to-shoulder, to help grow the operations of great companies solving the world's biggest problems. We invest in technology and technology-enabled companies that innovate and disrupt existing industries — from biosciences to transportation to food to health and wellness. We've had the honor of serving some of the world's greatest entrepreneurs and companies, including Tesla, SpaceX, Anduril, Eight Sleep, GoPuff, and others.

Our values are core to all we do. These values are excellence, humility, integrity, and responsibility.

Valor means that we:

  • Strive for excellence in everything we do;
  • Maintain our humility and mutual respect no matter what circumstances we encounter;
  • Insist upon the highest level of integrity in our interactions and in the logic of our investment process; and
  • Demonstrate responsibility and dedication to all of our constituents.

On the Valor Labs Team, we develop cutting edge machine learning models to derive proprietary investment insights and build software applications to augment the Firm's investment decision making process. As a small team of software engineers and data scientists with diverse backgrounds, we work collaboratively on wide-ranging problems to deliver high-impact products for the Firm.

As a Software Engineer on our data science and machine learning team, you will contribute directly to the development of high-impact products. Working together with data scientists, engineers, and stakeholders, you will translate complex project requirements into actionable technical solutions and work collaboratively to build, deploy, monitor, and maintain those solutions in production. Your technical expertise and commitment to excellence will help drive the adoption of best practices and ensure the highest level of rigor in everything we do.

About You:

  • B.S. in Computer Science or related field
  • 5+ years of experience developing production-ready software systems
  • Although not necessary, prior work experience in financial services is highly valued
  • Expertise in end-to-end machine learning operations: model deployment, monitoring, and retraining, supporting integration with production data pipelines and API services.
  • Proficient with Python, especially machine learning libraries like NumPy, Pandas, Scikit-Learn, and PyTorch
  • Proficient with SQL, including transactional (e.g., PostgreSQL) and analytical (e.g., BigQuery) databases
  • Professional experience with most, if not all, of the following:
    • Containerization (e.g., Kubernetes and Docker)
    • Data processing (e.g., Prefect, Airflow, and dbt)
    • Parallel processing (e.g., Ray, Dask, and Spark)
    • Cloud infrastructure (e.g., Google Cloud Platform)
    • Continuous integration/continuous deployment (e.g. GitHub Actions)
    • Infrastructure as code (e.g., Terraform)
    • Tools to support machine learning operations (e.g., MLFlow and DVC)
  • Humble, hard-working, and collaborative

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