Forward Deployed Engineer
Job description
About the role
You own the design and execution of proof-of-value engagements that demonstrate how Ray and the Anyscale platform solve critical machine learning workflow challenges for customer teams. You translate business priorities into technical solutions that maintain confidence in Ray while scaling adoption across all organizational levels. Through direct interaction with customer teams, you capture field insights that actively shape product direction and go-to-market strategy, particularly in Latin America and other key regions. You partner closely with data analysts, data scientists, and data engineers to ensure aligned outcomes across the data lifecycle. Your work drives measurable business outcomes by turning complex ML infrastructure into clear return on investment and strategic impact. You act as a trusted advisor to both technical practitioners and executive stakeholders, guiding them through tailored communication. Frequent travel and extended on-site engagements are required to ensure successful solution adoption. You contribute to scalable machine learning adoption by evangelizing best practices and platform capabilities in the field.
Key facts
What you'll do
Orchestrate proof-of-value engagements, deployments, and enterprise adoption initiatives that drive business outcomes for key customers.
Translate business objectives into technical solutions that demonstrate clear return on investment and strategic impact for customer organizations.
Design and execute scalable machine learning workflows using Ray to address distributed training, model serving, and inference demands.
Evaluate and optimize performance and cost tradeoffs for ML workloads running in Kubernetes-based container orchestration environments.
Partner with field teams to convert business goals into infrastructure and platform solutions that support enterprise adoption.
Communicate complex technical concepts to both technical practitioners and leadership stakeholders across diverse regions and cultures.
Guide customers through the adoption of complex SaaS, infrastructure, and ML/AI solutions within large, enterprise organizations.
Apply a customer-first mindset to build measurable business impact through scalable AI infrastructure deployments.
Tailor communication to address distinct priorities and decision-making processes for executive and technical audiences.
Conduct frequent travel and extended time embedding with customers to ensure solution success and long-term value.
Leverage the Ray framework to ramp quickly on real-world machine learning use cases and drive platform confidence.
Support go-to-market strategy by providing field insights that influence product direction and scalable ML adoption efforts.
Collaborate with data teams to align proof-of-value results with broader data strategy and business priorities.
Document and share successful deployment patterns and best practices to enable replication across new customer engagements.
Requirements
The posting states a bachelor's degree requirement.
Fluency in Spanish and English enables direct work with technical and executive stakeholders in Latin America and other regions.
Five or more years of customer-facing experience in forward deployed engineering, solutions architecture, field engineering, or software engineering builds a strong foundation.
You must possess the Ray framework applies to real-world machine learning use cases with demonstrated ability to ramp quickly.
ML training and inference workloads involve distributed training, model serving, and understanding performance and cost tradeoffs.
Kubernetes-based environments host deployed workloads using container orchestration knowledge.
Enterprise adoption of complex SaaS, infrastructure, or ML/AI solutions progresses within large customer organizations.
Communication tailors to executive and technical audiences, addressing distinct priorities and decision-making processes.
Measurable business impact emerges through a customer-first mindset in scalable AI infrastructure deployments.
Frequent travel and extended time embed with customers to ensure solution success.
A degree is required, along with willingness to travel as specified in the role.
Nice to have
A degree is required, along with willingness to travel as specified in the role.
Skills & tools
Ray powers distributed workloads for companies building and serving machine learning models.
Developers use Kubernetes to orchestrate containerized applications at scale.
Machine learning workflows involve training and inference across distributed systems.
Technical advisors communicate with both technical practitioners and leadership stakeholders.
Field engagements translate business goals into infrastructure and platform solutions.
Practical notes
Meet these requirements as outlined, and 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.
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.
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.