Everyone is welcome at Handshake. We know diverse teams build better products and we are committed to creating an inclusive culture built on a foundation of respect for all individuals. We strongly encourage candidates from non-traditional backgrounds, historically marginalized or underrepresented groups to apply.

Want to learn more about what it's like to work at Handshake? Check out these interviews from our team members!

Your impact

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.

Your Experience

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

Compensation range

$150,000-$220,000

For cash compensation, we set standard ranges for all U.S.-based roles based on function, level, and geographic location, benchmarked against similar stage growth companies. In order to be compliant with local legislation, as well as to provide greater transparency to candidates, we share salary ranges on all job postings regardless of desired hiring location. Final offer amounts are determined by multiple factors, including geographic location as well as candidate experience and expertise, and may vary from the amounts listed above.

About us

Handshake is the #1 place to launch a career with no connections, experience, or luck required. The platform connects up-and-coming talent with 750,000+ employers - from Fortune 500 companies like Google, Nike, and Target to thousands of public school districts, healthcare systems, and nonprofits.