Glynn Capital
Glynn Capital

Software Engineer, ML Infrastructure



Software Engineering, Other Engineering, Data Science
San Francisco, CA, USA · Remote
Posted on Saturday, April 20, 2024

At Instabase, we're passionate about democratizing access to cutting-edge AI innovation to enable any organization to solve previously unsolvable unstructured data problems in their industry. With customers representing some of the largest and most complex organizations in the world, and investors like Greylock, Andreessen Horowitz, and Index Ventures, our market opportunity is near infinite.

With offices in San Francisco, New York, London and Bengaluru, Instabase is a truly global company. We are people-first, and we've built a fearlessly experimental, endlessly curious, customer obsessed team who work together and help organizations around the world turn their unstructured data into insights instantly.

This is a hybrid role to be based in our San Francisco office.

Our Engineering Team architects the underlying core services, platform infrastructure, dev toolkits, core algorithms, machine learning models, packaged end-user apps, and app store marketplace. Instabase engineers are excited to solve hard problems for complex organizations and are self-starters from day one. As part of our Machine Learning Infrastructure Team, you’ll design and develop the next generation of machine learning products at Instabase. We are bridging the human-machine gap in ML, enabling humans to understand, debug, and fine-tune models, all the while deploying and managing these models at large scales (millions of requests per month).

As a Software Engineer working on Instabase products, you’ll build intuitive applications that empower our customers to leverage the latest technologies in AI/ML to tackle their hardest document understanding problems. Our tools and platform shine when facing highly unstructured documents. Our infrastructure is written in Go, Python and operates using the micro-services model. We use Docker and Kubernetes for our deployments.

What you'll do

  • Design and implement architectures for using, testing, and training models at scale, both in the cloud and on customer premises using Kubernetes.
  • Design and develop and contribute to scalable distributed systems infrastructure that power the ML/AI infrastructure.
  • Dive into the complexities of real-time data processing and develop strategies to ensure that our systems can efficiently handle the dynamic outputs of generative AI models.
  • Design and implement best practices for model management and deployment.
  • Create products around models that make it easy for the customer to use and understand machine learning models and approaches.
  • Work with both internal and external developers / data scientists to bring models into Instabase that are then used by customers to solve use cases.
  • Troubleshoot production issues and contribute to improving our platform stability.

About you

  • You have 3+ years experience as a software engineer.
  • You enjoy thinking about how the end user / customer interacts with and understands models.
  • You like getting to the bottom of deep, complex problems. You aren’t satisfied with “it works” until you understand why.
  • You are familiar with both distributed systems and data science, and enjoy thinking about how the two are built together.
  • You have experience with proper software engineering best practices.
  • Experience with Kubernetes, existing ML scaling techniques and model training/serving technologies like AnyScale Ray, vLLM, AWS Sagemaker preferred
  • Experience with machine ]learning (ML) and artificial intelligence (AI) in the space of document understanding preferred

The on-target earnings (OTE) for this role is $175,000 to $190,000 + equity and US benefits. The actual pay may vary based on factors such as location, experience and skills.


Instabase is an Equal Opportunity Employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender perception or identity, national origin, age, marital status, protected veteran status, or disability status. Research shows that in order to apply for a job, women feel they need to meet 100% of the criteria while men usually apply after meeting about 60%. Regardless of how you identify, if you believe you can do the job and are a good match, we encourage you to apply.