{"id":861783,"url":"https://alion.io/job/myfundedfutures-junior-machine-learning-engineer","title":"Junior Machine Learning Engineer","company":{"id":679031,"name":"MyFundedFutures","domain":"myfundedfutures.com","url":"https://alion.io/company/myfundedfutures","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"C","score":67,"open_postings":11,"ghost_share":0.545,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-16T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Plano, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":88000,"max_usd":194000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":348},"experience_years_min":null,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"CI/CD","optional":true},{"name":"Databricks","optional":true},{"name":"Docker","optional":true},{"name":"LightGBM","optional":true},{"name":"pySpark","optional":true},{"name":"Python","optional":true},{"name":"Snowflake","optional":true},{"name":"Spark","optional":true},{"name":"SQL","optional":true},{"name":"XGBoost","optional":true}],"status":"live","first_seen_at":"2026-09-01T14:56:37Z","employer_posted_date":"2026-09-01","last_verified_at":"2026-10-16T09:50:44Z","board_verified":true,"closed_at":null,"days_open":44,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":44},"description":"At My Funded Futures, we’re transforming the world of proprietary trading by giving traders the capital, tools, and community they need to succeed.\nWe blend innovation, transparency, and performance to create opportunity - helping traders scale faster and smarter. If you’re passionate about fintech, financial markets, and data-driven growth, you’ll fit right in.\nExplore our open roles below and see how you can help us shape the future of funded trading.\nThe Junior ML Engineer will support the Company's data science function by developing, validating, and maintaining machine learning models and the data pipelines behind them. This is a hands-on, applied role: you will work with real data on problems that directly shape the product and the business, and you will be expected to explain what your models do and why they can be trusted.\nYou will partner closely with the Data Science and Analytics team and with stakeholders across the organization, translating business questions into well-defined analytical problems and presenting results in terms decision-makers can act on.\nThis role is ideal for someone early in their career who has already built and shipped machine learning models and who wants broader exposure across modeling, analytics, and data engineering.\nKey Responsibilities\nDevelop, test, validate, and maintain machine learning models under the guidance of senior team members.\nBuild and maintain data pipelines and analytical datasets on the Company's cloud data platform.\nEvaluate model performance rigorously and document assumptions, methods, and limitations.\nSupport statistical analysis, forecasting, and experimentation to inform business decisions.\nPresent technical findings clearly to non-technical audiences.\nContribute to standards for model documentation, validation, and monitoring.\nQualifications\nBachelor's degree (or equivalent) in computer science, mathematics, engineering, or a related field, with coursework in machine learning or statistical learning. Graduate degree is a plus.\nStrong Python and PySpark skills, with the ability to write clean, tested, maintainable code.\nHands-on experience with a cloud data platform (Databricks, Snowflake, Fabric, or similar)\nStrong SQL, including window functions and multi-table joins.\nSolid understanding of core ML concepts: cross-validation, overfitting, class imbalance, data leakage (including in time-ordered data), and choosing evaluation metrics appropriate to the problem.\nHands-on experience with: \nGradient-boosted trees (XGBoost, LightGBM)\nLogistic regression, support vector machines, k-nearest neighbors\nClustering methods (k-means and others)\nExperience with some of the following: survival / time-to-event analysis, experiment design and causal inference, simulation and Monte Carlo methods, probability calibration, Bayesian or hierarchical modeling, model monitoring and drift detection\nExperience taking a model from development into a scheduled or production environment\nDocker, CI/CD, and workflow orchestration experience\nAbility to explain model behavior, including feature importance, calibration, and limitations.\nAbility to gather and present technical results to a non-technical audience.\nProven experience as a machine learning engineer or in a similar role is a plus.\nFintech, trading, or financial services background is a plus.\nEEO Statement \nEqual Employment Opportunity\nMy Funded Futures is an equal opportunity employer. We believe that diversity drives innovation and success. We are committed to building an inclusive environment where every team member feels valued, respected, and supported-regardless of race, color, religion, gender, gender identity, sexual orientation, national origin, age, disability, veteran status, or any other protected characteristic.\nPay Transparency \nIn compliance with pay transparency laws, My Funded Futures provides compensation ranges in job postings where required. Final compensation may vary based on experience, qualifications, and location. We also offer comprehensive benefits and performance-based incentives.\nAccessibility / Accommodation Statement\nIf you require assistance or an accommodation during the application process, please contact our HR team at . \nWork Authorization \nApplicants must be authorized to work in the applicable country without employer sponsorship. 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