Responsibilities
- Own and contribute to foundational models (e.g. CLIP embeddings) that powers our recommendations pipelines.
- Contribute to the research and development of recommender models as well experiment with the latest ML innovations (e.g. LLM agents and transcription models)
- Design, advocate, and implement for availability, scalability, operational excellence, and cost management while delivering impact to our daters incrementally.
- Collaborate closely with ML Engineers, Data Scientists, and Product Managers to understand their needs and identify opportunities to accelerate the AI/ML development and deployment process.
- Mentor and educate ML Engineers on current and up and coming research, technologies and best practices of doing ML at scale.
- Perform other job-related duties as assigned.
What We're Looking For
- Strong programming skills: Proficiency in languages like Python, Java or C++
- System design & architecture: Proven track record of training and deploying large scale ML models specially DNNs. Good understanding of distributed computing for learning and inference.
- Cloud platform proficiency: The ability to utilize cloud environments such as GCP, AWS, or Azure. Familiarity with ML serving solutions like Ray, KubeFlow or W&B is a plus.
- ML knowledge: Deep understanding of DNN architectures, track record of building, debugging and fine tuning models. Familiarity with PyTorch, TF, knowledge distillation, recommender systems are a plus.
- Dev-ops skills: The ability to establish, manage, and use data and compute infrastructure such as Kubenetes and Terraform.
- Data engineering knowledge: Skills in handling and managing large datasets including, data cleaning, preprocessing, and storage. Deep understanding of batch and streaming pipelines as well as orchestrators like Argo and Airflow.
- Collaboration and communication skills: The ability to work effectively in a team and communicate complex ideas clearly with individuals from diverse technical and non-technical backgrounds..
- Strong written communication: The ability to communicate complex ideas and technical knowledge through documentation
- Software leadership skills: A track record of leading projects through completion with quantifiable and measurable outcomes.
- 3+ years of experience, depending on education, as an MLE.
- 2+ years of experience working on a cloud environment such as GCP, AWS, Azure, and with dev-ops tooling such as Kubernetes
- 1+ year of experience leading projects with at least 1 other team member through completion.
- 2+ years of experience for designing and developing online and production grade ML systems.
- A degree in computer science, engineering, or a related field.
Top Skills
What We Do
In today’s digital world, singles are so focused on sending likes and looking through profiles that they’re not actually building meaningful connections and going on dates. Hinge is on a mission to change that by designing the most effective app experience. We want to create a less lonely world by inspiring intimate, in-person connections. Relationships are at the core of everything we do. And not just the romantic kind. We can't accomplish really hard things alone - so we make great relationships the foundation of our teamwork.
We believe these three core values are what it takes to build those great relationships: Authenticity, we share - never hide - our words, actions, and intentions. Courage, breakthroughs require a willingness to take risks and embrace lofty goals and tough challenges. Empathy, we're all humans first. So we deeply consider the perspectives of others, listen openly, and speak with care.
Why Work With Us
We're mission-driven. While most apps think about boosting sessions and time on app, we think strategically about meaningful end results (dates and relationships).
We're culture-first. We believe in great people over process. Decisions are pushed to the front lines, with feedback and coaching provided by our leaders.
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Hinge Offices
Hybrid Workspace
Employees engage in a combination of remote and on-site work.
Hinge believes in the power of in-person connection. We have adopted a hybrid model that allows our people to stay connected to each other in-person.