Software Engineer II, Machine Learning Palo Alto, California Department Engineering

Position: Software Engineer II, Machine Learning Hot Job  Palo Alto, California  Department Engineering

The Team:

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 Machine Learning Infrastructure Engineers who build the platforms and tools that enable scalable training, serving, and feature management Machine Learning Software Engineers who bridge the gap between research and production by delivering machine learning models into real-world product experiences at scale

About the Role:

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.

Where

You'll Work:

This is a hybrid role and requires in-office collaboration three times per week in Palo Alto, California.

In this role, you will:

• 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

You’ll need:

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

Nice to have:

• 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 Tensor Flow Serving, Torch Serve, 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

Salary Range:

$145,000 – $165,000 a year

Factors such as scope and responsibilities of the position, candidate's work experience, education/training, job-related skills, internal peer equity, as well as market and business considerations may influence base pay offered. This salary will be subject to a geographic adjustment (according to a specific city and state), if an authorization is granted to work outside of the location listed in this posting.

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