Senior AI Product Engineer, Arize AI
Backend
$125-225k
+ Equity
More information about location
Machine learning observability platform
Open for applications
Machine learning observability platform
21-100 employees
Open for applications
$125-225k
+ Equity
More information about location
21-100 employees
Company mission
To make the world’s AI work and work for the people.
Role
Who you are
- 5+ years of experience working with high-performance backend systems
- Strong experience writing Go, Python, TypeScript/Node, Java, or similar server programming languages
- Enthusiasm and interest in the AI and LLM ecosystem, with a desire to learn and stay updated on emerging technologies
- Previous work building and operating highly complex SaaS platforms/systems
- Knowledge of working with public clouds & container orchestration - AWS, GCP, Azure, Kubernetes, etc
Desirable
- Experience with distributed stream processing - Kafka, Gazette, or similar
- Experience with OLAP systems
- Familiarity with system observability tooling like Prometheus
- Working knowledge of Machine Learning and/or Data Science
- First-hand experience working with large language models (LLMs) or developing AI products
What the job involves
- Our Backend Engineering team builds all of the highly scalable distributed services that power Arize’s ML observability platform
- While Go is our primary language for these distributed systems, the team also maintains services and tools written in Python, Java, and TypeScript
- The expectation and scope of every individual on this team is high, whether it’s finding the most efficient way to compute model evaluation metrics across billions of data points, designing the next generation of our OLAP database architecture, or researching and implementing the latest dimensionality reduction techniques – you will never lack a technical challenge
- You will be a part of the core team that drives product innovation at Arize. You will be challenged with understanding how some of the most impactful engineering teams are developing AI and LLM-powered applications, and how to build the right tools to enable them to do their best work
- Write maintainable, scalable, and performant backend code primarily in Go, Java, and Python, with opportunities to work in TypeScript
- Build high-volume and highly available analytics systems
- Design and build APIs specific to our customers’ Machine Learning and LLM workflows
- Prototype, optimize, and maintain scalable backend services that power the Arize core platform
- Extend, and contribute back to, open source OLAP databases and distributed message queue frameworks
- Develop and integrate collection tools for robust monitoring of ML and LLM pipelines
- Research and implement cutting-edge visualization & dimensionality reduction algorithms in a distributed environment
- Collaborate with our product, design, and directly with customer engineering teams to enhance and expand our product offerings
- Contribute to the build our own in-house AI Agents
Our take
Machine learning is rapidly becoming a fundamental and mainstream automation tool used across many industries. The issue, however, is that monitoring, assessing, and improving ML models is costly, and especially tricky without monitoring capabilities. It’s created a market demand for ML monitoring tools, and Arize’s platform offers a rich suite of them.
The Arize platform gives developers and operators comprehensive oversight of their models with tools including performance tracing, drift detection, and integration and deployment. This provides a crucial guardrail: as more decision-making is handed over to ML in areas like insurance and loans, poor ML model performance can have a catastrophic effect on both individuals and customers alike.
Remarkably, given this development, ML observability platforms aren’t yet in huge supply. Arize startup rivals like Censius and WhyLabs are making strong headway, but a clear winner has by no means yet emerged. Arize has, however, netted some major customers: Spotify, Instacart, Uber, and P&G are a handful of the major clients that will help it lay a strong foundation in what is anticipated to be a highly lucrative market.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
Company
Funding (last 2 of 3 rounds)
Sep 2022
$38m
SERIES B
Sep 2021
$19m
SERIES A
Total funding: $61m
Company benefits
- Remote work opportunities
Company values
- Integrity - Do what you say, say what you do
- Balance - Hold space for what matters to you
- Candor - Be open, be honest, be constructive
- Humility - Keep a learning mindset
Company HQ
Mill Valley, CA
Articles
Leadership
Jason Lopatecki
(CEO)Previously a System Architect at Calix, before they moved on to found and serve as CSO/CIO at TubeMogul for over a decade.
Aparna Dhinakaran
(CPO)Has held Software Engineer roles at both Dote and Apple, and worked on Machine Learning at TubeMogul and Uber. Also co-founded and served as CEO at Monitor ML, and is additionally a columnist at Forbes.
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