• Develop and maintain large-scale distributed machine learning systems using frameworks like TensorFlow, PyTorch, and Scikit-Learn • Build predictive models including churn prediction, user journey analysis, and sales forecasting using behavioral data • Work with supervised and unsupervised learning, survival analysis, time series modeling, and statistical forecasting techniques • Collaborate with business units to understand their ML needs • Optimize feature extraction, transformation, and selection while managing Feature Stores for reusability across ML pipelines • Strong focus on MLOps practices including model training, versioning, monitoring, and deployment using CI/CD pipelines, Docker, Kubernetes, Airflow, SageMaker, and MLflow • Ensure scalability, reliability, cost efficiency, and ease of use of the machine learning platform while maintaining model observability and connecting outcomes to product and strategic goals

Role Description This role offers the opportunity to lead the development and deployment of advanced forecasting models that directly impact business planning, supply chain efficiency, and operational outcomes. You will own forecasting end-to-end—from model design and evaluation to production deployment and monitoring—while collaborating closely with business stakeholders, engineers, and the broader analytics team. The position emphasizes the use of rigorous evaluation, probabilistic modeling, and automated workflows to improve forecast accuracy and reliability. Ideal candidates thrive in a data-driven, fast-paced environment, enjoy solving complex problems, and are passionate about translating technical solutions into actionable business insights. Exposure to modern AI-assisted coding tools and agentic development workflows will accelerate the development of scalable, production-ready forecasting systems. - Design, develop, and maintain forecasting models across multiple horizons and business segments to improve accuracy and usability - Own end-to-end forecasting system lifecycle, including deployment, monitoring, refresh cadence, reliability, and iteration - Build and maintain evaluation infrastructure for forecasting, including backtesting, error analysis, uncertainty quantification, automated tests, and regression checks - Partner with stakeholders in operations, supply chain, inventory management, finance, and marketing to translate business needs into technical plans and measurable impact - Implement agentic workflows to automate repetitive forecasting tasks, ensuring reproducibility, human oversight, and measurable quality gates - Define success metrics tied to business outcomes and communicate assumptions, limitations, and risks to stakeholders - Continuously improve model performance, reliability, and stakeholder usability through iterative updates and monitoring Qualifications - 5+ years of professional experience in machine learning, applied data science, or forecasting, including deploying models into production or operational workflows - Strong expertise in time-series forecasting, feature engineering, evaluation/backtesting, and probabilistic modeling - Proficiency in Python and SQL, with the ability to write production-quality, testable software - Strong probability and statistics fundamentals, with experience communicating uncertainty in practical terms - Demonstrated ability to collaborate effectively with cross-functional and non-technical stakeholders - Preferred: PhD or equivalent experience in a quantitative discipline, experience with hierarchical/probabilistic forecasting, evaluation infrastructure, AI-assisted or agentic development workflows, and integrating forecasts with optimization or decision science Benefits - Competitive salary range of $160,000 – $210,000 plus annual profit award or bonus eligibility - Comprehensive healthcare coverage including medical, dental, and vision - Retirement savings plans (401(k) with pre-tax and Roth options) - Paid time off, educational assistance, and student loan repayment programs - Short- and long-term disability, life/AD&D insurance, and flexible spending accounts - Exposure to cutting-edge AI and ML workflows, and a collaborative, data-driven work culture - Opportunities for career growth, mentorship, and working on high-impact forecasting systems Company Description

Highlights: Role: Senior ML-Engineer Location: Georgia, Remote Language: Russian-speaking team; Strong English required (B2) About Us Fundraise Up is a modern fundraising platform built to make donating to nonprofits as fast and convenient as possible. We continuously innovate to reduce page load times, boost conversion rates, and support a wide range of payment methods. Each month, people around the world contribute tens of millions of dollars through our platform. The world’s leading nonprofit organizations trust Fundraise Up. UNICEF, the most prominent UN charity, uses our platform for 100% of its online fundraising. So does the American Heart Association, the Alzheimer’s Association, and many others. We’re proud to maintain a 4.9 out of 5 rating on leading review platforms. We serve the enterprise segment, with a primary client base in the US, Canada, UK, and Australia. The Team Our product development team is currently at 150+ and growing. Team members are located across Spain, Serbia, Poland, Portugal, Turkey, Cyprus, Georgia and Armenia. We primarily communicate in Russian. We’re a tight-knit, high-impact team where every task matters. It’s a dynamic, collaborative environment where smart, curious engineers support one another, share knowledge, and strive for excellence. We encourage open dialogue and host bi-weekly engineering meetups to explore technical topics and showcase team insights. About the Role We’re looking for an ML Engineer with 5+ years of production experience to strengthen our ML team. We operate as an internal service and centre of excellence for 10+ product teams at Fundraise Up. This means you won’t be tied to a single feature: one day you might be optimising donation amounts and upsell offers, and the next you could be building a smart assistant for the admin panel or working on transaction classification. We actively use not only classical ML, but also RL, and we’re expanding our LLM-based solutions (generation, classification, agents). That’s why we’re looking for someone with a broad mindset who isn’t afraid to experiment and can choose the most effective approach for each task. The project’s main audience and business team are based in the US. Although the product development team is Russian-speaking, you may occasionally need to write in English. What You’ll Do - Develop and deploy ML solutions across different business areas using tabular data (uplift models, recommender systems). We are also actively developing projects that will involve e-commerce mechanics. - Select the most appropriate ML/LLM approaches or propose alternative solutions. - Build end-to-end ML solutions: data preparation, training, API development, and monitoring. - Design LLM-powered features: from simple classifiers and content generation to complex AI assistants and chatbots. - Work across the full LLM lifecycle: golden datasets, prompt engineering, fine-tuning, and response evaluation. Requirements - 5+ years of ML/DS experience solving real product problems - Strong expertise in ML and mathematical statistics: solid knowledge of classical algorithms (especially gradient boosting) and understanding of modern NLP/LLM approaches - Metrics-driven mindset: ability to connect ML metrics (ROC-AUC, F1, RMSE) with business metrics (CR, LTV) - Strong engineering culture: confident in Python with a product-oriented approach to development; we value clean code, knowledge of design patterns, and solid engineering practices - Data skills: advanced SQL; ability to independently and efficiently build complex datasets in ClickHouse and work with MongoDB - MLOps understanding: hands-on experience with experiment tracking tools and understanding of production workflows (Docker, Git, CI/CD) - Autonomy: ability to break down problems, choose the right tech stack (or justify a non-ML solution), and deliver to production Our Tech Stack Core: Python (uv, ruff), FastAPI, Pydantic, Docker Models: CatBoost, Uplift Modeling (CausalML), OpenAI (RAG, Prompt-Engineering) Data: ClickHouse, MongoDB, pandas, Polars, Redis MLOps: MLflow, Airflow Monitoring: Grafana, Sentry Bonus points - Curiosity and a hypothesis-driven mindset - Ability to communicate complex analytical concepts to non-technical audiences - Detail-oriented with a strong sense of ownership - Comfort working in fast-paced, data-rich environments Why work with us - A strong, collaborative product team that owns what it builds - Clear product vision and access to real customer feedback from global nonprofit leaders - Flat structure: no politics, just great work with great people - Transparent company culture-we share how we’re growing, where revenue comes from, and what’s next - Long-term focus: we offer equity options and value sustained, meaningful contribution Benefits - 31 days off - 100% paid telemedicine plan - Home Office Setup Assistance: the company offers assistance with purchasing furniture (office chair, office desk, monitor) and other items to create a comfortable workspace. - English learning courses - Relevant professional education - Gym or swimming pool - Co-working - Remote working **Please note: All official correspondence from Fundraise Up will exclusively originate from the @fundraiseup.com domain. Exercise caution and ensure the authenticity of emails claiming to be from our company. We are an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of race, color, religion, gender, sexual orientation, gender identity or expression, age, national origin, disability, or any other characteristic protected by applicable law in the countries where we operate.

Machine Learning Engineer Hybrid Engineering Full time White Plains, New York, United States Description Who We Are and Why Join Us OnMed our purpose is simple but powerful...to improve the quality of life and sense of well-being in our communities by bringing access to healthcare to everyone, everywhere. Our path to everywhere has already begun, with our innovative OnMed CareStation™, a small but mighty, Clinic-in-a-Box, bringing #healthcareaccess anywhere with an outlet to plug it in. Poised to become a key component in America’s public health infrastructure, the CareStation is the only tech-enabled, human-led, hybrid care solution that combines the comprehensive experience, trust and outcomes of a clinic, with the rapid scalability of virtual care. At OnMed, every role, every day, is directly impacting the communities we serve. You’ll join a high-performing purpose-driven team, innovating to break down the barriers that keep people from the care they need. This is not just a job...it's a movement to bring access to healthcare where and when people need it most. It’s healthcare that shows up Who You Are You are a Data Scientist / ML Engineer who bridges the gap between rigorous statistical science and production-grade engineering. You design and validate models, run controlled experiments, and ship reliable, maintainable ML systems—working side by side with a software development team in a shared toolchain. You leverage AI-aided development practices to responsibly speed development, while applying your expertise in understanding the sensitivity of specific models and the need for clinical accuracy. You are skilled in proper validation techniques and identifying potential biases or weaknesses in underlying data, particularly as models are developed to support clinical diagnoses. You take full ownership of your work—from hypothesis through deployment. This is not a role where you hand off notebooks to an engineering team; you see your work through end-to-end. Requirements Role’s Responsibilities - Design and execute experiments with proper controls, including variable isolation, hypothesis testing, and statistical power analysis. - Build, validate, and monitor machine learning models using sound. statistical methodology (cross-validation, confidence intervals, residual analysis, distributional checks, etc.) - Write production-quality Python code, structured for maintainability, testability, and peer review. - Develop and run ML workloads in distributed compute environments (Spark / PySpark), including feature engineering, large-scale data processing, and model training pipelines. - Comfortable explaining the tradeoffs of AI-assisted development in the data science / ML/AI development space, and comfortable leveraging AI to speed model development. - Collaborate within a standard SDLC: branching strategies, pull requests, code review, and CI/CD pipeline participation. - Partner closely with data engineers, software engineers, and analysts — contributing to shared codebases and following team conventions. - Document work clearly: experiment design docs, model cards, and pipeline documentation. Knowledge, Skills & Abilities - Strong proficiency in Python for ML and data workflows. - Experience with a deep learning or ML framework — PyTorch, TensorFlow, or similar. - Demonstrated ability to design rigorous experiments and statistically validate model results (not just optimize a metric). - Hands-on experience with Apache Spark / PySpark for large-scale data processing. - Comfortable working in Git — branching, PRs, conflict resolution, and code review as standard practice. - Familiarity with CI/CD concepts and participation in automated build/test/deploy pipelines. - Ability to communicate technical findings clearly to both technical and non-technical stakeholders. - Nice to haves: - Exposure to containerization (Docker) and/or orchestration basics. - Experience ina regulated or data-sensitive industry (healthcare, finance, etc.) - Background in feature stores, data versioning, or ML platform tooling. Education & Experience - Bachelor's or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related technical field. - 5–7 years of hands-on experience in a data science or ML engineering role. - Experience working in a cloud-based data platform (Databricks, AWS SageMaker, Azure ML, or similar). Benefits The base salary range for this role is $170,000 commensurate with the candidate's experience. OnMed is a proud equal opportunity employer. All qualified applicants will be considered without regard to race, color, creed, religion, gender, gender identity or expression, sexual orientation, national origin, genetic information, disability, age, marital status, veteran status, or any other category protected by law.