Staff Machine Learning Infrastructure Engineer
Confirmed live in the last 24 hours
Handshake

501-1,000 employees

Career launch platform connecting students with employers
Company Overview
Handshake stands out as a leading career platform, bridging the gap between over 13 million students and alumni from diverse educational backgrounds and more than 850,000 employers worldwide, including Fortune 500 companies and public institutions. The company's culture fosters inclusivity and accessibility, enabling individuals to launch their careers regardless of their connections or experience. With a robust technical platform that simplifies the application process and a global presence, Handshake demonstrates industry leadership in connecting talent with opportunity.
Education

Company Stage

Series F

Total Funding

$434M

Founded

2014

Headquarters

San Francisco, California

Growth & Insights
Headcount

6 month growth

-4%

1 year growth

-2%

2 year growth

30%
Locations
San Francisco, CA, USA
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Kubernetes
Apache Flink
Apache Beam
BigQuery
Apache Spark
SQL
Docker
Pandas
Elasticsearch
Natural Language Processing (NLP)
Data Analysis
Google Cloud Platform
CategoriesNew
AI & Machine Learning
DevOps & Infrastructure
Software Engineering
Requirements
  • ML Infrastructure Expertise: Proven ability in designing, implementing, and managing, complex Machine Learning pipelines including:
  • Feature Store deployment (Online and Offline)
  • Real-Time/Nearline Data Workflows in support of low latency inference
  • Cluster management and deployment, deployment of GPU based training jobs
  • Familiarity with ML feature tools such as Pinecone, Elasticsearch, etc
  • Technological Mastery: Deep understanding of ML tools, frameworks, and technologies such as Spark, Pandas, Torch etc., particularly their applications in machine learning.
  • Machine Learning Aptitude: Demonstrable experience in applying machine learning techniques to enhance data engineering tasks, with emphasis on model training and deployment.
  • Generative AI (LLMs): Familiarity with large language models such as ChatGPT, LLaMa, or Bard for text generation and Natural Language Processing (NLP) tasks.
  • Cloud Platform Expertise: Hands-on experience with cloud-based data technologies, preferably Google Cloud Platform (GCP). This includes tools like BigQuery, and Cloud Storage, and a ML stack (Vertex, Ray, or similar) for handling machine learning workflows.
  • SQL Mastery: Strong expertise in SQL with significant experience in data modeling and database design principles geared towards optimizing machine learning tasks.
  • Problem-Solving Prowess: Outstanding problem-solving skills, with the ability to navigate complex machine learning infrastructure challenges and propose innovative, effective solutions.
  • Teamwork Oriented: A collaborative approach to work, coupled with the ability to communicate complex machine learning concepts effectively to both technical and non-technical stakeholders, and take input from relevance stakeholders to guide implementation details
Responsibilities
  • Drive the architecture, implementation, and evolution of the Machine Learning platform
  • Build a robust platform on top of the data platform to empower Machine Learning and Relevance teams
  • Develop offline and online model serving capabilities
  • Work across a wide range of technical challenges and deployment strategies
Desired Qualifications
  • Containerization and orchestration: Familiarity with containerization technologies like Docker and container orchestration platforms like Kubernetes.
  • Streaming data processing: Experience with streaming data processing platforms such as Apache Beam or Apache Flink