Software Engineer II, Machine Learning

Match Group•Palo Alto, CA

1d•$145,000 - $165,000•Hybrid

About The Position

The Tinder ML team drives impact across nearly every core domain of the product — Recommendations, Trust & Safety, Profile, Chat, Growth, and Revenue optimization. Our mission is to apply machine learning to enhance user experiences, foster trust, and accelerate business growth across Tinder’s ecosystem. ML at Tinder is organized into three groups with distinct roles: Machine Learning Engineers who focus on modeling and algorithmic innovation (this role), Machine Learning Infrastructure Engineers who build the platforms and tools that enable scalable training, serving, and feature management, and Machine Learning Software Engineers who bridge the gap between research and production by delivering machine learning models into real-world product experiences at scale. We are looking for a Machine Learning Engineer II to help build and ship machine learning systems that improve product experience and drive measurable business impact. This role is ideal for an engineer with a strong foundation in machine learning and software engineering who is excited to work on real-world problems, partner cross-functionally, and grow quickly in a high-impact environment. This is an individual contributor role focused on modeling and algorithmic innovation. You will work closely with product, engineering, data, and platform partners to translate product opportunities into machine learning solutions, run experiments, and help bring models from development into production. The team’s work directly translates into measurable business outcomes, and many of its models are embedded in core Tinder user flows at scale.

Requirements

BS or MS in Computer Science, Machine Learning, Statistics, Mathematics, or a related technical field
2+ years of industry experience in machine learning, software engineering, data science, or a related field
Strong foundation in computer science fundamentals, including data structures, algorithms, and software design
Experience building ML or AI-related systems, or strong understanding of how modern machine learning systems are developed and operated
Proficiency in Python and at least one additional programming language such as Java, Kotlin, Go, Scala, or a similar language
Strong understanding of machine learning fundamentals, including model training, evaluation, and experimentation
Strong communication skills and the ability to collaborate effectively across functions
Self-motivated, proactive, and comfortable taking ownership of well-scoped problems

Nice To Haves

Experience with recommendation systems or casual inference
Familiarity with big data or stream processing frameworks such as Spark or Flink
Familiarity with cloud platforms such as AWS and containerized environments such as Kubernetes
Familiarity with ML model serving frameworks such as TensorFlow Serving, TorchServe, Triton Inference Server, or Ray Serve
Experience with feature stores, ML data pipelines, and orchestration frameworks such as Airflow
Understanding of MLOps practices including CI/CD for ML, model versioning, and automated evaluation
Exposure to observability and monitoring for ML systems
Exposure to LLM-related use cases or applied generative AI projects

Responsibilities

Translate product and business problems into clear machine learning problems with measurable success criteria
Build, train, evaluate, and improve production machine learning models
Partner with software engineers and ML infrastructure engineers to deploy models and improve reliability, scalability, and performance in production
Design and analyze offline evaluations and online experiments to understand model impact
Contribute to feature engineering, data preparation, training pipelines, and model monitoring
Write clean, maintainable, production-quality code and participate in design and code reviews
Communicate technical findings, trade-offs, and recommendations clearly to both technical and non-technical partners