Machine Learning Sales Engineer, Arize AI
Machine learning observability platform
Open for applications
Machine learning observability platform
21-100 employees
Open for applications
21-100 employees
Company mission
To make the world’s AI work and work for the people.
Role
Who you are
- You can empathize with the frame of reference of who you are communicating with and tailor your message and approach accordingly
- Ability to simplify complex, technical concepts
- You are undaunted by the technical complexity of production ML deployments and welcome the challenge to learn about them and develop your own POV
- You ask the right questions with the customer to uncover nuances in their unique deployments
- Ability to work within ambiguity and take action with limited direction
- You may not have a PhD in ML but you know the difference between Linear Regression and Boosted Trees and the advantages / disadvantages of each
- You have some experience training models in common packages such as scikit-learn, HuggingFace, fastai, and etc
- 5+ years within customer facing role:
- Pre-sales, technical account management, or consulting experience
- Worked customers within the high enterprise (e.g. Fortune 500, etc)
- Proficiency in: Python Linux/Unix
- Previous experience working across and aligning Sales, Product, and Engineering
- Previous experience within a team that underwent a high growth stage
- Previous engineering experience in: Data Engineering, MLOps, Kubernetes, GCP / AWS / Azure
What the job involves
- You are the trusted advisor to the customer:
- Build relationships with technical stakeholders
- Lead product demonstrations of the Arize platform
- Lead discovery to understand prospect’s ML stack to collaborate with the Sales team to construct a compelling value proposition of the Arize Platform
- Handle technical objections and develop strategies across sales, engineering, and product to unblock them
- Act as a Domain Expert within AI/ML:
- Write educational and compelling blog posts about ML and MLOps related topics
- Collaborate to create and enhance documentation, recorded video assets and other publically available as well as internal enablement materials
- Engage in relevant ML communities online to raise awareness on challenges of deploying ML in production
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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