Staff Machine Learning Platform Engineer

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.