Machine Learning Engineer II

About the role

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.

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

Requirements

• BS or MS in Computer Science, Machine Learning, Statistics, Mathematics, or a related technical field
• 1+ year 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

Qualifications

• Self-motivated, proactive, and comfortable taking ownership of well-scoped problems

Skills

• 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

Benefits

• Flexible vacation
• 10 sick days
• Time off to volunteer and charitable donations matched up to $15,000 annually
• Comprehensive health, vision, and dental coverage
• 100% 401(k) employer match up to 10%
• Employee Stock Purchase Plan (ESPP)
• 100% paid parental leave (including for non-birthing parents) and family forming benefits
• Investment in your development: mentorship through our MentorMatch program, access to 6,000+ online courses through Udemy, and an annual $3,000 stipend for your professional development
• Investment in your wellness: access to mental health support via Modern Health, paid concierge medical membership, pet insurance, fitness membership subsidy, and commuter subsidy
• Free subscription to Tinder Gold

Pay

$145,000 - $165,000

Schedule

Hybrid role requiring in-office collaboration three times per week in Palo Alto, California.