Staff Machine Learning Platform Engineer

19 Hours ago • 5 Years + • $244,000 PA - $293,000 PA

Job Summary

Job Description

The Staff ML Platform Engineer will drive the design, development, and evolution of Hinge's Feature Store platform. Responsibilities include owning streaming offline and online feature store capabilities, enabling Machine Learning Engineers (MLEs) to efficiently perform data exploration and feature engineering operations. The role involves collaboration with various teams, including ML engineers, data scientists, and product managers, to ensure the Feature Store scales to meet the growing data demands and satisfies data privacy requirements. The engineer will also be responsible for defining the long-term roadmap, evaluating new technologies, and mentoring others.

Must have:

5+ years of experience as an ML Platform Engineer
4+ years of experience with cloud environments such as GCP, AWS, Azure, and Kubernetes
3+ years of experience leading projects
2+ years of experience designing ML Feature Store systems
Strong programming skills: Proficiency in languages like Python, Go, or Java.
System design & architecture: Ability to design scalable and efficient ML systems
Data engineering expertise: Skills in handling large streaming data processing systems and formats
Feature Store Platform technology skills: The ability to establish and use Feature Store platforms
Cloud platform proficiency: The ability to utilize cloud environments such as GCP, AWS, or Azure
ML knowledge: Broad awareness of the entire ML lifecycle
Communication skills: The ability to communicate complex ideas clearly
Software leadership skills: A track record of leading projects with multiple contributors
Strategic leadership skills: Demonstrated technical leadership experience in aligning platform strategy

Good to have:

Streaming Data skills: The ability to establish and utilize Streaming data processing frameworks
Data warehousing skills: The ability to establish and use Data warehousing platforms
Dev-ops skills: The ability to establish, manage, and use data and compute infrastructure
Strong collaboration skills: A track record of creating and sustaining a healthy team culture
Vendor Management: Experience working with vendors, identifying vendor risks

15 skills required

15 skills required for this role

Add these skills to join the top 1% applicants for this job

kubernetes

azure

aws

communication

team-management

java

github-actions

github

spark

python

docker

terraform

json

leadership

scalability

Job Details

Hinge is the dating app designed to be deleted

In today's digital world, finding genuine relationships is tougher than ever. At Hinge, we’re on a mission to inspire intimate connection to create a less lonely world. We’re obsessed with understanding our users’ behaviors to help them find love, and our success is defined by one simple metric– setting up great dates. With tens of millions of users across the globe, we’ve become the most trusted way to find a relationship, for all.

About the Role

Hinge is hiring an experienced Staff ML Platform Engineer to drive the design, development and evolution of our Feature Store platform. You will own our streaming offline and online feature store capabilities, enabling Machine Learning Engineers (MLEs) to efficiently perform data exploration and feature engineering operations and utilize features for model training and model inference (batch, near real-time and online). You will collaborate closely with ML engineers, data scientists, data engineers, partner platform teams and project managers to ensure that our Feature Store scales to meet the growing data demands of our ML teams, provides intuitive workflows for feature management and satisfies requirements for data privacy and legal frameworks at Hinge.

This role requires awareness and empathy for the applied AI/ML problem space. You will ensure that the Feature Store platform is truly self-service and serves the evolving needs of all ML stakeholders without incurring a linear operations burden. You will also be deeply integrated with the rest of the AI platform and understand data access patterns across the entire ML lifecycle. Your success will depend on maintaining a cohesive, end-to-end view of how data is used in early model experimentation, training, evaluation and inference in production. Being part of a small yet highly impactful team means having a broad scope of responsibility, and as ML is still in its early stages at Hinge, this role provides a chance to grow as a technical leader by mentoring others on the team and across the company. This is an exciting opportunity to own and help define the future of machine learning within a rapidly growing team!

Responsibilities

Define the long-term, holistic roadmap for the Feature Store platform, aligning it with company-wide ML initiatives and ensuring end-to-end integration with model training, serving and observability platforms.
Evaluate and introduce new technologies, tools and best practices that enhance feature serving reliability, scalability, cost efficiency and throughput, including leading build vs buy discussions.
Architect, build, and maintain frameworks enabling MLEs for self service data ingestion and serving pipelines for both offline (batch, async) and online (low-latency) feature stores.
Partner with cross-functional Platform teams to represent feature engineering requirements and incorporate them into Hinge’s wider Platform capabilities.
Collaborate closely with ML Engineers, Data Scientists, and Product Managers to understand the ML development lifecycle and identify opportunities to accelerate the AI/ML development and deployment process.
Mentor and educate ML Engineers and Data Scientists on current and up and coming methods, tools and technologies for Feature Engineering.
Help design and architect an AI platform that adheres to the principles of responsible AI and simplifies privacy compliance.

What We're Looking For

5+ years of experience, depending on education, as an ML Platform Engineer, Data Engineer, or Platform Engineer developing and working with large scale, complex data processing and or warehousing systems.
4+ years of experience working on a cloud environment such as GCP, AWS, Azure, and with dev-ops tooling such as Kubernetes
3+ years of experience leading projects with at least 2 other team members through completion.
2+ years of experience for Staff designing and developing online and production grade ML Feature Store systems.
A degree in computer science, engineering, or a related field.

Strong programming skills: Proficiency in languages like Python, Go, or Java.
System design & architecture: Ability to design scalable and efficient ML systems, particularly data intensive systems.
Data engineering expertise: Skills in handling and managing large streaming data processing systems and formats (parquet, json, protobuf, delta) including data cleaning, preprocessing and storage systems.
Feature Store Platform technology skills: The ability to establish and use Feature Store platforms such as Databricks, Feast, Tecton, Hopsworks, Ray, and/or similar.
Cloud platform proficiency: The ability to utilize cloud environments such as GCP, AWS, or Azure.
ML knowledge: Broad awareness of the entire ML lifecycle, including the data needs for training, serving and evaluation.
Communication skills: The ability to communicate complex ideas clearly with individuals from diverse technical and non-technical backgrounds through documentation, RFCs and presentations.
Software leadership skills: A track record of leading projects with multiple contributors and stakeholders through completion with quantifiable and measurable outcomes.
Strategic leadership skills: Demonstrated technical leadership experience in aligning platform strategy with product and business objectives.

Even Better With...

Streaming Data skills: The ability to establish and utilize Streaming data processing frameworks like Kafka, Kafka Streams, Flink, Spark Streaming, Kinesis, etc.
Data warehousing skills: The ability to establish and use Data warehousing platforms (BigQuery, Databricks, Snowflake, Redshift).
Dev-ops skills: The ability to establish, manage, and use data and compute infrastructure such as Argo, Airflow, Docker, Github Actions, Kubernetes, and Terraform.
Strong collaboration skills: A track record of creating and sustaining a healthy team culture of mentorship, psychological safety, accountability. Skills to level up and act as a force-multiplier for others.
Vendor Management: Experience working with vendors, identifying vendor risks and advocating for team/stakeholder priorities to get onto their roadmaps.