Principal Machine Learning Engineer

Description

The role sits in the team building the cloud-native infrastructure behind the company's next generation of Agentic AI and conversational products.

You will set direction and architecture across several engineering organizations and still write and debug code yourself on hard platform and scale problems.

The company is moving to next-generation architectures and needs a principal engineer to help steer that work.

Responsibilities

• Set the technical direction and architecture for cloud-native platform services that support Agentic AI work.
• Design and build Kubernetes-based infrastructure that handles AI/ML and conversational workloads at enterprise scale.
• Work with engineering, infrastructure, product, and platform teams to modernize key services.
• Build distributed systems that are highly available, secure, observable, and resilient for mission-critical customer workloads.
• Define architecture standards, engineering practices, and reusable platform patterns for several product teams.
• Automate provisioning, deployment pipelines, service operations, monitoring, and incident response.
• Assess new tools and technologies in cloud-native infrastructure, Kubernetes, AI platforms, and distributed systems, and adopt the ones that fit.
• Mentor senior engineers and guide technical decisions across organizational boundaries without formal authority.
• Lead architecture reviews covering scalability, reliability, performance, security, and operations.
• Work closely with teams building AI-powered experiences so the infrastructure can support their growth.
• Balance long-term platform investment against near-term product delivery with stakeholders across the company.
• Take technical initiatives from concept through production and help shape the direction of the Agentic AI platform.

Requirements

• 15+ years building large-scale distributed systems, cloud platforms, infrastructure services, or enterprise SaaS products.
• Deep expertise in Java, Go, Python, or a similar systems programming language.
• A record of designing, building, and running highly scalable cloud-native architectures.
• Expert knowledge of Kubernetes, containers, orchestration platforms, and modern cloud infrastructure.
• Experience building platform services that several engineering teams and products depend on.
• Solid understanding of distributed systems, including resiliency, fault tolerance, scalability, performance tuning, and observability.
• Experience running workloads at large scale on AWS, Azure, or Google Cloud.
• Expertise in CI/CD, infrastructure automation, GitOps, Terraform, Helm, ArgoCD, or comparable tools.
• Strong software engineering fundamentals: object-oriented design, system design, architecture patterns, and performance optimization.
• Experience setting technical strategy and shaping architecture across several organizations without direct authority.
• A proven record of taking new platform capabilities from concept to wide production use.
• Clear communication, including explaining technical tradeoffs to engineers and executive leadership.
• Preferred qualification: experience building infrastructure for AI/ML, generative AI, agentic systems, or conversational platforms.
• Preferred qualification: experience supporting GPU workloads, model serving, inference platforms, or AI runtime environments.
• Preferred qualification: experience with internal developer platforms, platform engineering, SRE, or cloud infrastructure organizations.
• Preferred qualification: knowledge of multi-agent architectures, orchestration platforms, workflow engines, or large-scale automation systems.
• Preferred qualification: experience with observability platforms such as OpenTelemetry, Prometheus, Grafana, Datadog, or equivalent.
• Preferred qualification: experience modernizing legacy architectures into cloud-native distributed services.

Conditions

Base pay for this role in this location is $240,100 to $420,200, plus equity where applicable, variable or incentive pay, and benefits.

Actual total pay depends on qualifications, skill level, competencies, and work location, and the base range is a guideline that may change with work location.

Sales positions generally include an On Target Earnings (OTE) incentive structure.

Work persona is flexible, remote, or required in office, assigned by the nature of the work and work location.

Eligibility for a work persona may be checked by measuring the distance from your home to the nearest company office with a third-party service.

Benefits

• Health plans, including flexible spending accounts.
• 401(k) plan with company match.
• Employee stock purchase plan (ESPP).
• Matching donations.
• Flexible time away and family leave programs.