Senior Software Engineer - Machine Learning and AI

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.

Requirements:

- 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.

Benefits:

- 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.