Software Engineer II, Machine Learning

Our Mission As humans, there are few things more exciting than meeting someone new. At Tinder, we’re inspired by the challenge of keeping the magic of human connection alive. With tens of millions of users, hundreds of millions of downloads, 2+ billion swipes per day, 20+ million matches per day, and a presence in 190+ countries, our reach is expansive—and rapidly growing. We work together to solve complex problems. Behind the simplicity of every match, we think deeply about human relationships, behavioral science, network economics, AI and ML, online and real-world safety, cultural nuances, loneliness, love, sex, and more. Our Values Take the Lead: We don't ghost our work or each other. Just as users don't leave their matches hanging, we don't let each other down. Move Fast: We have a bias for action and urgency. Something that could be done tomorrow would be better if done today. Better Together: We keep connection at the heart of dating and at the heart of how we work. Just as our users are better when they connect with others, so are we when we collaborate. Real Talk: We say the hard thing the human way. Just as we ask our users to behave with kindness and candor in our community, we expect Team Tinder to do the same. Safety First: We act with integrity, transparency, and consistency so people feel safe—whether they're swiping, matching, or working alongside us. Spark Fun: We have fun to unlock creativity, fuel innovation, and help us build better experiences for daters. The Team or 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. 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 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. As a full-time employee, you’ll enjoy: 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 Commitment to Inclusion At Tinder, we don’t just accept difference, we celebrate it. We strive to build a workplace that reflects the rich diversity of our members around the world, and we value unique perspectives and backgrounds. Even if you don’t meet all the listed qualifications, we invite you to apply and show us how your skills could transfer. Tinder is proud to be an equal opportunity workplace where we welcome people of all sexes, gender identities, races, ethnicities, disabilities, and other lived experiences. Learn more here: https://www.lifeattinder.com/dei 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 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

{
  "content_hash": "48c4876447acb5b3a227de980f933db089954724d1abfe02f24ef2b44d647962",
  "source_hash": "cf1d90421c4acd5de42ea94b6b30698e6bb7d3a9a501c9bf757c3ec85e464fec",
  "last_changed_at": "2026-06-04T11:36:10.625Z",
  "active_status": "active"
}

{
  "language": "en",
  "location": {
    "raw": "Palo Alto, California",
    "city": "Palo Alto",
    "region": "CA",
    "country": "United States",
    "is_remote": false,
    "confidence": 0.85
  },
  "salary_max": 165000,
  "salary_min": 145000,
  "inferred_at": "2026-06-06T07:57:18.918Z",
  "launch_scope": {
    "reason": "english_us_canada",
    "included": true,
    "language": "en",
    "location": {
      "raw": "Palo Alto, California",
      "city": "Palo Alto",
      "region": "CA",
      "country": "United States",
      "is_remote": false,
      "confidence": 0.85
    },
    "countries": [
      "United States"
    ]
  },
  "remote_policy": "hybrid",
  "salary_period": "year",
  "workplace_type": "hybrid",
  "salary_currency": "USD"
}

{}

{
  "lists": [
    {
      "text": "In this role, you will: ",
      "content": "<div>\n\n<li>\n<p>Translate product and business problems into clear machine learning problems with measurable success criteria</p>\n</li>\n<li>\n<p>Build, train, evaluate, and improve production machine learning models</p>\n</li>\n<li>\n<p>Partner with software engineers and ML infrastructure engineers to deploy models and improve reliability, scalability, and performance in production</p>\n</li>\n<li>\n<p>Design and analyze offline evaluations and online experiments to understand model impact</p>\n</li>\n<li>\n<p>Contribute to feature engineering, data preparation, training pipelines, and model monitoring</p>\n</li>\n<li>\n<p>Write clean, maintainable, production-quality code and participate in design and code reviews</p>\n</li>\n<li>\n<p>Communicate technical findings, trade-offs, and recommendations clearly to both technical and non-technical partners</p>\n</li>\n\n</div>"
    },
    {
      "text": "You'll need: ",
      "content": "<div>\n\n<li>\n<p>BS or MS in Computer Science, Machine Learning, Statistics, Mathematics, or a related technical field</p>\n</li>\n<li>\n<p>1+ year of industry experience in machine learning, software engineering, data science, or a related field</p>\n</li>\n<li>\n<p>Strong foundation in computer science fundamentals, including data structures, algorithms, and software design</p>\n</li>\n<li>\n<p>Experience building ML or AI-related systems, or strong understanding of how modern machine learning systems are developed and operated</p>\n</li>\n<li>\n<p>Proficiency in Python and at least one additional programming language such as Java, Kotlin, Go, Scala, or a similar language</p>\n</li>\n<li>\n<p>Strong understanding of machine learning fundamentals, including model training, evaluation, and experimentation</p>\n</li>\n<li>\n<p>Strong communication skills and the ability to collaborate effectively across functions</p>\n</li>\n<li>\n<p>Self-motivated, proactive, and comfortable taking ownership of well-scoped problems</p>\n</li>\n\n</div>"
    },
    {
      "text": "Nice to have:",
      "content": "<div>\n\n<li>\n<p>Experience with recommendation systems or casual inference</p>\n</li>\n<li>\n<p>Familiarity with big data or stream processing frameworks such as Spark or Flink</p>\n</li>\n<li>\n<p>Familiarity with cloud platforms such as AWS and containerized environments such as Kubernetes</p>\n</li>\n<li>\n<p>Familiarity with ML model serving frameworks such as TensorFlow Serving, TorchServe, Triton Inference Server, or Ray Serve</p>\n</li>\n<li>\n<p>Experience with feature stores, ML data pipelines, and orchestration frameworks such as Airflow</p>\n</li>\n<li>\n<p>Understanding of MLOps practices including CI/CD for ML, model versioning, and automated evaluation</p>\n</li>\n<li>\n<p>Exposure to observability and monitoring for ML systems</p>\n</li>\n<li>\n<p>Exposure to LLM-related use cases or applied generative AI projects</p>\n</li>\n\n</div>"
    }
  ],
  "country": "US",
  "createdAt": 1780436922512,
  "updatedAt": null,
  "categories": {
    "team": "Engineering",
    "location": "Palo Alto, California",
    "department": "Tinder",
    "allLocations": [
      "Palo Alto, California"
    ]
  },
  "salaryRange": {
    "max": 165000,
    "min": 145000,
    "currency": "USD",
    "interval": "per-year-salary"
  },
  "workplaceType": "hybrid"
}