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Private Equity Machine Learning Jobs (NOW HIRING)

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 ...

Pluralsight LLC

Machine Learning Engineer

$121K - $160K/yr

A Machine Learning Engineer helps our learners discover content that is relevant to their interests ... Learn more about our commitment to diversity, equity, inclusion, and belonging in our DEIB Report.

Hive

Machine Learning Engineer

Seattle, WA

$120K - $180K/yr

Machine Learning Role In order to execute our vision, we need to grow our team of best-in-class ... Hired applicant may receive an equity grant in the form of an option to purchase stock in the ...

Spotify

Machine Learning Engineer

New York, NY · On-site +1

$170K - $212K/yr

We're looking for a Machine Learning Engineer to help us build systems that more accurately ... The United States base range for this position is $170,000 - $212,000 plus equity. The benefits ...

Robinhood

Machine Learning Engineer

Bellevue, WA · On-site

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 ...

Spotify

Machine Learning Engineer

New York, NY · On-site +1

$170K - $212K/yr

We're looking for a Machine Learning Engineer to help us build systems that more accurately ... The United States base range for this position is $170,000 - $212,000 plus equity. The benefits ...

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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.

Machine Learning Engineer

Machine Learning Engineer

Robinhood

Bellevue, WA

Other

Medical, Life, Retirement, PTO

Posted 5 days ago

Job description

Join us in building the future of finance.
Our mission is to democratize finance for all. An estimated $124 trillion of assets will be inherited by younger generations in the next two decades. The largest transfer of wealth in human history. If you're ready to be at the epicenter of this historic cultural and financial shift, keep reading.
About the team + role
We are building an elite team, applying frontier technologies to the world's biggest financial problems. We're looking for bold thinkers. Sharp problem-solvers. Builders who are wired to make an impact. Robinhood isn't a place for complacency, it's where ambitious people do the best work of their careers. We're a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards. We're looking for an exceptional Machine Learning Engineer to help shape the future of our core platforms, products, and customer experiences. FinTech is one of the most complex and rapidly evolving spaces in technology, and the challenges we're tackling require deep innovation, critical thinking, and scale that don't always have strong precedents.
You'll take on a highly influential role shaping vision and execution across key strategic initiatives. You'll partner with cross-functional leaders, contribute to high-impact decisions, guide complex projects from concept to completion, and mentor others on the team. This is a role for someone who leverages modern tools and cutting-edge methodologies as a core part of how they solve problems, and raises the bar for everyone around them.
This role is based in our Bellevue, WA, with in-person attendance expected at least three days per week.
At Robinhood, we believe in the power of in-person work to accelerate progress, spark innovation, and strengthen community. Our office experience is intentional, energizing, and designed to fully support high-performing teams.
What you'll do
As a Machine Learning Engineer on the AI Research and Development team, the primary focus will be on the implementation and evaluation of machine learning algorithms through rigorous experimentation and testing methodologies.
The responsibilities will include:
  • AI and ML Research: Evaluate cutting technologies, including but not limited to, transformer-based model architecture and large foundational models to identify solutions for Robinhood specific problems.
  • Model Development and Implementation: Develop and implement scalable machine learning models focusing on advanced ranking and recommendation systems, including expertise in Collaborative Filtering, Content Based Filtering, and Hybrid models, alongside proficiency in Learning to Rank (LTR) techniques for effective prioritization. Additionally, design reinforcement learning algorithms and apply multi-armed bandit strategies to optimize decision-making in dynamic environments, balancing exploration and exploitation.
  • A/B Testing and Experimentation: Design and conduct A/B tests to assess the performance of different machine learning models. This includes setting up the test environment, monitoring performance, and analyzing results.
  • Data Analysis and Insight Generation: Analyze experimental data to extract actionable insights. Use statistical techniques to validate the findings and ensure their relevance and accuracy.
  • Cross-Functional Collaboration: Work closely with other engineering teams, data scientists, and the marketing team to integrate machine learning models into the product and ensure they meet business requirements. Present results to different stakeholders.
  • Tooling and Documentation: Build reusable libraries for common machine learning practices. Offer support and guidance to the usage of these tools. Maintain comprehensive documentation of libraries, models, experiments, and findings.
  • Telecommuting permitted.
What you bring
  • Bachelor's degree or foreign equivalent in Computer Science or related field and three years (3) of experience in job offered or related occupation. Alternatively, a Masters in Computer Science or related field and one year (1) of experience in job offered or related occupation
  • Education and/or experience must include:
    • Productionisation of ML models with focus on recommendations, ranking, or personalization;
    • Model development with classical ML techniques for tabular data;
    • Model development with modern ML techniques for sequential data;
    • Hands-on experience with architectural frameworks of large, distributed, and high-scale ML applications;
    • Produce robust business outcomes through comprehensive AB test and rigorous statistical analysis;
    • Proficiency in Python, SQL, XGBoost, Pytorch or Tensorflow to carry out production ready projects; and
    • Spark, Kafka, or Kubernetes.
  • Background checks required.
What we offer
  • Challenging, high-impact work to grow your career
  • Performance driven compensation with multipliers for outsized impact, bonus programs, equity ownership, and 401(k) matching
  • Best in class benefits to fuel your work, including 100% paid health insurance for employees with 90% coverage for dependents
  • Lifestyle wallet - a highly flexible benefits spending account for wellness, learning, and more
  • Employer-paid life & disability insurance, fertility benefits, and mental health benefits
  • Time off to recharge including company holidays, paid time off, sick time, parental leave, and more!
  • Exceptional office experience with catered meals, events, and comfortable workspaces.

In addition to the base pay range listed below, this role is also eligible for bonus opportunities + equity + benefits.
Base pay for the successful applicant will depend on a variety of job-related factors, which may include education, training, experience, location, business needs, or market demands. The expected base pay range for this role is based on the location where the work will be performed and is aligned to the corresponding compensation zone.
Base Pay Range:
$161,138 - $200,000 per year
To Apply: Apply by clicking APPLY NOW. Indicate job code 10035097 in your application.
Click here to learn more about our Total Rewards, which vary by region and entity.
If our mission energizes you and you're ready to build the future of finance, we look forward to seeing your application.
Robinhood provides equal opportunity for all applicants, offers reasonable accommodations upon request, and complies with applicable equal employment and privacy laws. Inclusion is built into how we hire and work-welcoming different backgrounds, perspectives, and experiences so everyone can do their best. Please review the Privacy Policy for your country of application.

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