Want to learn more about what it's like to work at Handshake? Check out these interviews from our team members!
At Handshake, we are assembling a diverse team of dynamic engineers who are passionate about creating high-quality, impactful products. As an ML Infrastructure Engineer, you will play a key role in driving the architecture, implementation, and evolution of our rapidly growing Machine Learning platform. Your technical expertise and leadership will be instrumental in helping millions of students discover meaningful careers, irrespective of their educational background, network, or financial resources.
Our primary focus is on building a robust platform on top of our data platform that empowers our Machine Learning and Relevance teams to develop offline and online model serving capabilities, working across a wide range of technical challenges and deployment strategies.
To excel in this role, you should possess:
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
Bonus Areas of Expertise
While not required, expertise in any of the following areas would be highly advantageous:
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