Senior Software Engineer - Machine Learning and AI
New
J
JobgetherMachine learning
Based in United StatesFull-TimeSenior
Salary not disclosed
Apply NowOpens the employer's application page
Job Details
- Experience
- 2+ years of experience building and deploying machine learning systems in production
- Required Skills
- Machine LearningMLFlowPyTorchSparkTensorflowDeep Learningscikit-learn
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.
- Strong mathematical foundation in probability and statistics, linear algebra, and optimization.
- Hands-on experience with classical machine learning and deep learning, including approaches such as gradient-boosted trees, embedding models, and transformers.
- Experience with large-scale data processing, distributed training, and feature stores.
- Experience with modern ML tooling such as Spark, PyTorch or TensorFlow, scikit-learn, and MLflow or similar platforms.
- Demonstrated experience in recommender systems, fraud or risk modeling, search ranking, or real-time classification.
- Research experience through publications, thesis work, or applied research is a strong plus.
- Commitment to strong baselines, honest evaluation, reproducibility, and measurable outcomes.
- Ability to take ML systems from experimentation and design through production and ongoing performance.
Responsibilities
- Research, prototype, and evaluate computational, statistical, machine learning, and AI models using donor and transaction datasets.
- Translate product challenges into defined ML problems, success metrics, and evaluation methodologies.
- Design and productize machine learning systems for personalized recommendations.
- Build AI systems for real-time data applications including fraud prevention, risk scoring, and engineering reliability.
- Develop end-to-end ML pipelines for feature engineering, data processing, model training, evaluation, experimentation, A/B testing, and deployment.
- Build and operate real-time inference services with Platform and DevOps teams, focusing on scalability, latency, reliability, and monitoring.
- Own production model quality and reliability, balancing product objectives with false positives and negative user experiences.
- Ensure model behavior aligns with responsible product principles and supports trustworthy experiences.
- Mentor engineers and strengthen scientific rigor, reproducibility, and code quality.
View Full Description & ApplyYou'll be redirected to the employer's site
