AI Solutions Engineer, Arize AI
$125-175k
+ Competitive equity package
Machine learning observability platform
Be an early applicant
Machine learning observability platform
21-100 employees
Be an early applicant
$125-175k
+ Competitive equity package
21-100 employees
Company mission
To make the world’s AI work and work for the people.
Role
Who you are
- Note: Even if you do not check every single box, we still encourage you to apply!
- Previous experience working as a Data Scientist, Machine Learning Engineer, or as an Engineer working with ML models or GenAI applications in production
- Comfortable working in public Cloud environments (AWS, Azure, GCP)
- Knowledge of machine learning frameworks such as TensorFlow, PyTorch or Scikit-learn
- Knowledge of LLM / Agentic frameworks such as Llamaindex, LangGraph, and DSPy
- Understanding of ML/DS concepts, model evaluation strategies and lifecycle (feature generation, model training, model deployment, batch and real time scoring via REST APIs) and engineering considerations
- Understanding of GenAI concepts and application evaluation + development lifecycle
- Proficiency in a programming language (Python, JS/TS, Java, Go, etc)
- Strong Communication Skills - Ability to simplify complex, technical concepts
- A quick and self learner - undaunted by technical complexity of production ML deployments and welcome the challenge to learn about them and develop your own POV
Desirable
- Previous engineering experience in:
- Data Science
- MLOps
- ML Frameworks
- LLM / Agentic frameworks
- Customer facing experience strongly preferred such as Solutions Architect, Implementation Specialist, Sales Engineer, Customer Success Engineer, Consultant, or Professional Service roles
- Prior experience working with applications deployed with Kubernetes
- Prior experience demoing technical products to both business and technical audiences
What the job involves
- Our engineering team builds systems that interact with some of the most complex software ever deployed in production
- The team is composed of industry veterans that have built deep learning infrastructure, autonomous drones, ridesharing marketplaces, ad tech and much more
- We are looking for a client-obsessed AI Solutions Engineer with entrepreneurial tendencies to join the good fight and help build out our Solutions Engineering org
- You’ll be the trusted technical advisors for our customers, driving business value, offering advice, and growing accounts
- You’ll accomplish this by leading customers to solutions oftentimes by teaching the product to new users or consulting on best practices
- You must be ready for technical discussions with data scientists and engineers, then demonstrate the value of Arize in business discussions with directors and executives
- The goal is to enable our customers to become successful and enthusiastic about Arize
- You will act as a trusted advisor to our customers, while also building relationships with technical and business stakeholders
- Advise on GenAI and ML best practices
- Give ML and LLM product demos to technical and business stakeholders
- Run strategic business reviews for customers in partnership with our sales team
- Interface with our pre-sales engineering team to gather client goals and KPI’s
- Partner with our product and engineering teams to help drive the product roadmap
- Spearhead new opportunities within existing accounts to help drive expansions
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
Meet the team
Meet Andy, Product Design Team Lead
Our mission
Collaborate to create functional and innovative design solutions.Insights
23% employee growth in 12 months
Company
Funding (last 2 of 4 rounds)
Feb 2025
$70m
SERIES C
Sep 2022
$38m
SERIES B
Total funding: $131m
Company benefits
- Remote work
- Open PTO policy
- Health, dental, vision insurance
- 401K
- Company wellness days
- Coworking
Company values
- Do > Say - We believe in building, not just talking. Execution matters more than meetings, and progress beats process. Instead of endless debate, we prototype. Instead of bureaucracy, we ship. AI moves fast—so do we.
- N+1 - Good enough isn’t good enough. We believe in continuous improvement—whether it’s improving AI-powered systems, our product, or ourselves, there’s always a better version to build, iterate, and level up.
- Bring Solutions - Spot a problem? Own it. We don’t waste time on complaints or excuses—we focus on fixing, improving, and building. Whether it’s a blocker in a project or a gap in how we work, the expectation is simple: push for a better way forward.
Company HQ
New York, NY 10006
Articles
Leadership
Jason Lopatecki
(Co-Founder & 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
(Co-Founder & 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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