Machine Learning Engineer, Customer Engineering

About the role

This position advances customer success on the Anyscale platform by resolving issues and guiding adoption. The role operates within a follow-the-sun support model and builds strong relationships with technical stakeholders.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Customer issues are resolved to enable successful adoption of the Anyscale platform.

Open bugs and feature requests are tracked to guide product prioritization and keep customers informed during resolution.

Continuity for high-priority tickets is maintained through a follow-the-sun support model.

Customer issues are owned end-to-end, from troubleshooting and triaging through escalations to final resolution.

Technical advice is provided to key customers, acting as an internal champion through proactive support.

Platform improvements are influenced by feedback from customer issues shared with product and engineering teams.

Technical stakeholders inside customer accounts are engaged to build strong relationships.

Internal tools, playbooks, guides, and best practices are improved based on patterns observed in support activities.

Requirements

Seven or more years of experience in a Machine Learning role in a dynamic, fast-paced, startup-like environment.

The ability to manage multiple customer needs at the same time is supported by strong organizational skills.

Proficiency in developing data pipelines covers training, fine-tuning, and inference or serving of LLMs.

Experience running and optimizing infrastructure for distributed ML workloads exists on AWS/EKS, GCP/GKE, or Azure/AKS.

Clear and effective communication is required, along with excellent interpersonal skills.

Self-motivation and a strong sense of ownership drive eagerness to acquire new skills and handle new challenges.

An interest in mentoring peers through mentorship, trainings, and shadowing helps elevate the knowledge of others.

Practical notes

This role is based in San Francisco and operates under E-Verify rules.

Anyscale is an equal opportunity employer that evaluates candidates without regard to protected characteristics.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

MLOps platforms help manage the lifecycle of machine learning models.

Container orchestration platforms like Kubernetes automate deployment and scaling.

Infrastructure as code tools define cloud resources using configuration files.

CI/CD tools automate software changes through testing and deployment pipelines.

Questions to ask

Useful questions for the interview: what a typical week looks like, how work is assigned, what tools the team uses, and how feedback works. Asking how the role has changed recently and what the team wishes it had known when joining is also reasonable. Questions about the manager's priorities are especially valued.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.

About the company

At Anyscale https://www. Anyscale. com/, we're on a mission to democratize distributed computing and make it accessible to software developers of all skill levels.