The ad says“Experience using data and deployment technologies (Snowflake, Airflow, Kubernetes, Docker, Helm, Spark, PySpark)”
The ad says“Experience using data and deployment technologies (Snowflake, Airflow, Kubernetes, Docker, Helm, Spark, PySpark)”
The ad says“Experience using data and deployment technologies (Snowflake, Airflow, Kubernetes, Docker, Helm, Spark, PySpark)”
The ad says“Experience building machine/deep learning models with at least one common framework such as PyTorch, Tensorflow, or Keras”
The ad says“Experience using data and deployment technologies (Snowflake, Airflow, Kubernetes, Docker, Helm, Spark, PySpark)”
The ad says“Expertise in building and maintaining LLM workflows for nuanced, human-driven tasks”
The ad says“Proven ability to deliver at scale, with fluency in Python and popular ML frameworks”
The ad says“Experience using data and deployment technologies (Snowflake, Airflow, Kubernetes, Docker, Helm, Spark, PySpark)”
The ad says“Proven ability to deliver at scale, with fluency in Python and popular ML frameworks”
The ad says“Experience building machine/deep learning models with at least one common framework such as PyTorch, Tensorflow, or Keras”
The ad says“Experience using data and deployment technologies (Snowflake, Airflow, Kubernetes, Docker, Helm, Spark, PySpark)”
The ad says“Experience using data and deployment technologies (Snowflake, Airflow, Kubernetes, Docker, Helm, Spark, PySpark)”
The ad says“Experience building machine/deep learning models with at least one common framework such as PyTorch, Tensorflow, or Keras”
Values
The ad says“Experience with any public cloud environment - AWS, GCP or Databricks”
The ad says“Experience with any public cloud environment - AWS, GCP or Databricks”
The ad says“Experience with any public cloud environment - AWS, GCP or Databricks”
Architect and oversee large-scale recommendation systems to serve millions, balancing performance and innovation
Leverage the latest LLMs to process vast conversation data and enhance user connections
Prototype, iterate, and ship production-ready ML solutions that solve real problems for our user
Provide technical leadership across teams, collaborating with engineering, data science and product teams to turn bold ideas into tangible results
Evaluate, influence, and integrate emerging AI tools and techniques at an organizational level to keep Grindr’s tech stack ahead of the curve
Benefits
100% employer-paid medical, dental, and vision premiums for employees
401k Matching (up to 6%)
Flexible PTO, sick-time, holidays, and two company wide shut-down weeks + an annual travel and leisure stipend
Equity for all employees
Monthly lifestyle stipend for food and wellness as well as fully catered onsite breakfast, lunch, and snacks
Gender Affirmation Fund for transgender and gender non-conforming employees
Queer-inclusive health benefits including LGBTQ consultative healthcare and expanded HRT shipped directly to employees in eligible states
Monthly commuter stipend to get you to and from the office on in-person collaboration days
Community projects and charitable giving stipend to support LGBTQ+ non-profit organizations alongside Grindr for Equality
Preferred experience
Experience with any public cloud environment - AWS, GCP or Databricks
A scrappy mindset—you tackle messy challenges head-on and get real results
Proven ability to deliver at scale, with fluency in Python and popular ML frameworks
Experience building machine/deep learning models with at least one common framework such as PyTorch, Tensorflow, or Keras
Experience using data and deployment technologies (Snowflake, Airflow, Kubernetes, Docker, Helm, Spark, PySpark)
10+ years of experience building ML systems with a focus on 0-to-1 system development, platform-scale architecture, and creating new capabilities from scratch. Experience with recommendation systems or similar is a plus
Expertise in building and maintaining LLM workflows for nuanced, human-driven tasks
A track record of leading the development of recommendation platforms that hit the mark for diverse user needs
Thrives in a fast-paced, outcome-driven environment with lots of cross-functional collaboration
A proven history of setting technical direction, driving adoption of ML systems across organizations, and owning products end-to-end from early-stage concept to full-scale rollout
