The posting
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Software Engineer - Machine Learning and AI based in United States.
As a Senior Software Engineer specializing in Machine Learning and AI, you’ll build and deploy intelligent systems that power real-time decisions across a large-scale digital giving platform. You’ll work on personalization, recommendations, fraud detection, risk scoring, and other high-impact applications of machine learning and AI. The role combines deep technical problem-solving with end-to-end ownership, from mathematical formulation and experimentation through production deployment. You’ll work with large-scale donor and transaction datasets while building reliable, low-latency services that operate at significant scale. Your models will directly influence how people discover meaningful causes, develop consistent giving habits, and experience safe and trusted transactions. As part of a research-focused engineering team, you’ll collaborate with experienced engineers and scientists while contributing to a purpose-driven, human-centered environment. The role also offers opportunities to mentor others, strengthen engineering and scientific standards, and solve complex problems where machine learning can create meaningful impact.
Accountabilities:
- Research, prototype, and evaluate computational, statistical, machine learning, and AI models using real-world donor and transaction datasets.
- Translate ambiguous product challenges into well-defined ML problems, including ranking, classification, anomaly detection, and recommendation use cases, with clear success metrics and evaluation methodologies.
- Design and productize machine learning systems that deliver personalized recommendations and help users connect with the organizations and causes most relevant to them.
- Build AI systems capable of processing large volumes of real-time data across applications such as fraud prevention, risk scoring, engineering reliability, and other business and product challenges.
- Develop and maintain end-to-end ML pipelines covering feature engineering, large-scale data processing, model training, evaluation, experimentation, A/B testing, and deployment.
- Build and operate real-time inference services in partnership with Platform and DevOps teams, ensuring scalability, low latency, reliability, and effective monitoring.
- Own the production quality and reliability of models, continuously balancing product objectives with false positives and negative user experiences.
- Ensure model behavior aligns with responsible product principles and supports positive, trustworthy experiences.
- Mentor engineers on machine learning best practices while raising standards for scientific rigor, reproducibility, and code quality.
- Master’s or PhD in Computer Science, Computer Engineering, Electrical Engineering, Statistics, Applied Mathematics, another STEM discipline, or equivalent research experience.
- 2+ years of experience building and deploying machine learning systems in production, ideally including systems serving hundreds of thousands to millions of users.
- Strong mathematical foundation in probability and statistics, linear algebra, and optimization, with an ability to understand the underlying principles behind model behavior.
- Hands-on experience with both classical machine learning and deep learning approaches, such as gradient-boosted trees, embedding models, and transformers, with sound judgment about when to apply each technique.
- Experience with large-scale data processing, distributed training, feature stores, and modern ML tooling such as Spark, PyTorch or TensorFlow, scikit-learn, and MLflow or similar platforms.
- Demonstrated experience in one or more relevant areas, such as recommender systems, fraud or risk modeling, search ranking, or real-time classification.
- Research experience through publications, thesis work, or applied research, with the ability to translate novel methods into production solutions, is a strong plus.
- Strong problem-solving skills, with the ability to approach complex challenges creatively using data, sound judgment, and collaboration.
- Scientific rigor, including a commitment to strong baselines, honest evaluation, reproducibility, and measurable outcomes.
- Strong ownership mindset and accountability for taking ML systems from experimentation and design through production and ongoing performance.
- Clear and effective communication skills, with the ability to collaborate across technical and product teams.
- A customer-focused and human-centered approach to building solutions that anticipate user needs and promote positive experiences.
- High integrity and sound judgment, with a commitment to acting responsibly and making principled decisions.
- Competitive compensation.
- Comprehensive benefits designed to support employees’ health and long-term well-being.
- Flexible paid time off.
- Additional employee perks and support programs.
- Fully virtual work environment with opportunities to collaborate with high-performing professionals.
- Meaningful work at the intersection of machine learning, AI, personalization, fraud prevention, and real-time decision-making.
- Opportunity to contribute to products designed around human needs and positive social impact.
- Collaborative and inclusive culture that values diversity, integrity, innovation, and purpose.
- Opportunities for professional growth, mentorship, and continuous learning.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Why Apply Through Jobgether?
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
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