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Full Time Machine Learning Researcher Jobs (NOW HIRING)

Detroit, MI- Onsite Type: Full-time Security Clearance: No clearance required, must be clearable. The Machine Learning Engineer will be an essential member of the Research and Development Team, where ...

ISEE is seeking a full-time Machine Learning Engineer to join our team. The ideal candidate has several years of work experience. Role responsibilities include: * Working on the intersection of ...

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Full Time Machine Learning Researcher information

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$30K

$113.1K

$164.5K

How much do full time machine learning researcher jobs pay per year?

As of May 30, 2026, the average yearly pay for full time machine learning researcher in the United States is $113,102.00, according to ZipRecruiter salary data. Most workers in this role earn between $67,000.00 and $154,000.00 per year, depending on experience, location, and employer.

What is the difference between Full Time Machine Learning Researcher vs Data Scientist?

AspectFull Time Machine Learning ResearcherData Scientist
CredentialsAdvanced degrees in CS, ML, or related fieldsDegree in CS, statistics, or related fields; often includes certifications
Work EnvironmentResearch labs, academic institutions, R&D departmentsBusiness environments, tech companies, analytics teams
Industry UsagePrimarily in research-focused roles, developing new algorithmsApplying models to solve business problems, data analysis

While both roles require strong technical skills and knowledge of machine learning, Full Time Machine Learning Researchers focus on developing new algorithms and advancing ML theory, often in research settings. Data Scientists apply existing models to analyze data and generate insights for business decisions. The roles overlap in skills but differ in focus and work environment.

More about Full Time Machine Learning Researcher jobs
What are the most commonly searched types of Machine Learning Researcher jobs? The most popular types of Machine Learning Researcher jobs are:
Infographic showing various Full Time Machine Learning Researcher job openings in the United States as of May 2026, with employment types broken down into 1% As Needed, 90% Full Time, 8% Part Time, and 1% Contract. Highlights an 43% Physical, 4% Hybrid, and 53% Remote job distribution, with an average salary of $113,102 per year, or $54.4 per hour.

Machine Learning Researcher (PhD) - Systematic Commodities Hedge Fund

Moreton Capital Partners

New York, NY โ€ข On-site

Full-time

Medical, Life, PTO

This job post hasย expired 1 day ago.ย Applications are no longer accepted.


Job description

Immediate Start - Machine Learning Researcher (PhD) โ€“ Systematic Commodities Hedge Fund

Moreton Capital Partners is rapidly expanding and seeking a talented Machine Learning Researcher to help design and improve the predictive models that power our systematic commodities trading strategies in our Mexico City Office.

We trade global commodity futures using machine learning, alternative data, and institutional-grade portfolio construction. Our edge comes from research depth, disciplined experimentation, and robust production systems.

This role is for candidates completing or having recently completed a PhD with a strong machine learning, statistics, or applied mathematics focus who want to apply advanced research in a real capital environment. You will work directly with the CIO and sit alongside a world class international quant research team to turn cutting-edge ML ideas into live trading signals. Your research will ship to production and directly impact portfolio returns.

This is not a purely academic role. We're looking for someone ready to hit the ground running and available to start immediately. In return, we offer a competitive salary, substantial performance share, comprehensive benefits, incredible work environment and a relocation package to make the move seamless.

What you will work on
  • Designing predictive models for cross-sectional and time-series commodity returns
  • Developing new features from price, positioning, options, macro, and alternative datasets
  • Improving signal robustness and reducing overfitting through rigorous validation
  • Combining and blending multiple models into portfolio-level forecasts
  • Regime detection, meta-models, and adaptive allocation frameworks
  • Model diagnostics, explainability, and stability analysis
  • Translating research ideas into production-ready implementations
  • Collaborating with engineers to deploy models into live trading systems
Key Responsibilities
  • Formulate research hypotheses and test them using clean, time-aware ML pipelines
  • Build and evaluate models (tree-based, linear, ensemble, deep learning, etc.)
  • Run walk-forward and out-of-sample experiments with realistic costs
  • Analyze information coefficients, turnover, drawdowns, and risk-adjusted returns
  • Design feature engineering frameworks and reusable research tooling
  • Document findings clearly and communicate results to portfolio managers
  • Contribute to improving research standards, reproducibility, and processes

Requirements

  • PhD (completed or near completion) in Machine Learning, Statistics, Applied Mathematics, Computer Science, Physics, Engineering, or related quantitative field
  • Strong Python skills and experience with scientific computing stacks
  • Deep understanding of statistical learning and model validation
  • Experience working with large datasets and experimental pipelines
  • Ability to move from theory to practical implementation
  • Intellectual curiosity and strong problem-solving mindset
  • Comfortable working in a fast-paced, high-ownership environment
Bonus Points For
  • Experience with financial markets or systematic trading
  • Familiarity with time-series modelling or forecasting
  • Experience with LightGBM/XGBoost, deep learning, or ensemble methods
  • Exposure to portfolio construction or risk modelling
  • Experience with cloud or distributed compute environments
  • Published research or strong applied projects
Why this role is unique
  • Direct impact: your research drives live trading capital
  • Research freedom: explore ideas with fast feedback loops
  • Real-world data: large, messy, multi-source datasets
  • Small team: high ownership and rapid iteration
  • Strong learning curve across ML, markets, and portfolio construction
  • Clear path into Senior Researcher or Portfolio Manager responsibilities

Benefits

  • Market leading benefits
  • High responsibility from day one
  • Attractive compensation: Highly competitive base salary and annual bonus that scales as the business grows.
  • Relocation package to our Mexico City office, along with a competitive benefits offering that includes health and life insurance, a year-end bonus, and generous paid time off.
  • Positive, inclusive and encouraging work environment.
  • Close collaboration across a global team.