Software Engineer, AI / ML Inference Platform

Dialpad; Buenos Aires; Presencial; ARS 181.094.000 - 271.641.000; Jornada completa

Descripción de la vacante

Dialpad is seeking ML Inference Platform Engineers to build the production systems that serve our in-house AI models at scale. This role lies at the intersection of model development, high-performance runtime systems, and cloud infrastructure.

You will help turn trained models and AI capabilities into reliable, observability-rich, low-latency services running on NVIDIA GPUs in GCP. This is an implementation-heavy role focused on inference machinery, deployment safety, and scalable production

Formación

• 6+ years of professional software engineering experience shipping backend services, infrastructure systems, or production platforms.
• Proficiency in writing maintainable production code in Python, Go, or another backend-oriented language.
• Experience building, operating, or optimizing high-throughput services, distributed systems, or ML infrastructure.
• Hands-on experience with containers, Kubernetes, Linux environments, CI/CD, deployment automation, and production operations.
• Strong instinct for reproducibility, observability, rollout safety, failure modes, and resilience.
• Comfort reasoning about bottlenecks across compute, memory, network, storage, batching, concurrency, and SLOs.
• Ability to work closely with model developers, product engineers, infrastructure teams, and leadership.

Responsabilidades

• Design, build, and improve systems connecting AI capability development to production inference.
• Develop inference serving and runtime systems for low-latency, high-throughput workloads.
• Operate and optimize GPU-backed containerized workloads on Kubernetes/GCP.
• Integrate model-serving frameworks and runtimes (e.g., vLLM, Triton, TGI) for internal deployment and observability.
• Enable shadow serving, canary rollouts, and safe rollback mechanisms for model-backed services.
• Build tooling to measure latency, throughput, cost, and reliability under production traffic.
• Improve packaging, versioning, deployment, and rollback of model artifacts across environments.
• Strengthen telemetry, logging, tracing, dashboards, and alerting for production insight.
• Contribute to compute efficiency and cost-performance tradeoffs across the platform.

Conocimientos

Production Engineering Experience

Strong Software Fundamentals

Inference or Systems Orientation

Kubernetes & Linux Fluency

Operational Judgment

Performance Awareness

Collaboration

Herramientas

vLLM

Triton

TGI

Descripción del empleo

About Dialpad

Dialpad is the AI platform for customer experience, built to resolve customer problems in real time across voice and digital. Our AI agents learn from your best human agents and improve with every interaction, helping organizations understand their customers, deliver better experiences, increase operational efficiencies, and build a lasting competitive advantage.

Unlike legacy systems built to route and answer, or standalone agentic bot vendors built to deflect, Dialpad was built to resolve. Our AI agents and human agents operate on a single platform with shared context, allowing Agentic AI to resolve issues, advance deals, and eliminate busywork through automation while seamlessly handing conversations to humans when needed, with full context preserved.

Market-leading brands, including Randstad, Motorola Solutions, Netflix, the San Diego Padres, the Colorado Rockies Baseball Club, and Cal Athletics, trust Dialpad. Dialpad is backed by Andreessen Horowitz, GV, ICONIQ Capital, and T-Mobile.

Being a Dialer

At Dialpad, AI isn’t just a feature; it’s how our teams do their best work every day. We put powerful AI tools in every employee’s hands so they can move faster, think bigger, and achieve more.

We believe every conversation matters. And we’ve built the platform that turns those conversations into insight and action, for our customers and ourselves.

We look for people who are intensely curious and hold themselves to a high bar. Our ambition is significant, and achieving it requires a team that operates at the highest level. We seek individuals who embody our core traits: Scrappy, Curious, Optimistic, Persistent, and Empathetic.

Your role

We are hiring ML Inference Platform Engineers to build the production systems that serve our in-house AI models at scale.

This role sits at the intersection of model development, high-performance runtime systems, and cloud infrastructure. You will help turn trained models and emerging AI capabilities into reliable, observable, low-latency production services running on NVIDIA GPUs in GCP.

This is not a research role, and it is not a generic MLOps or support role. It is an implementation-heavy systems engineering role focused on the machinery of inference: model serving, runtime optimization, GPU utilization, deployment safety, traffic management, benchmarking, and production reliability.

Our mission is to shorten the path from promising model capability to dependable production impact. We build the shared infrastructure, standards, and release pathways that allow models to move from candidate artifacts into scalable, rollback-safe inference services with clear performance, reliability, and cost characteristics.

This is a new team, so the systems and interfaces are still being shaped. You will help define how models are packaged, deployed, benchmarked, monitored, compared, and operated across environments. The work is practical, deeply technical, and closely tied to the company’s broader AI strategy. We are not building one-off demos; we are building the inference platform by which a growing AI organization can repeatedly and safely ship real model-backed products.

What you’ll do

• You will design, build, and improve the systems that connect AI capability development to production inference.
• Depending on your strengths, your work may include:
• Inference Serving & Runtime Systems: Build and improve model-serving pathways for low‑latency, high‑throughput, high‑availability inference workloads.
• GPU Infrastructure & Utilization: Operate and optimize containerized workloads on Kubernetes/GCP, with a focus on efficient use of NVIDIA GPUs, memory, storage, and networking.
• Model Server Integration: Work with model‑serving frameworks and runtimes such as vLLM, Triton, TGI, or similar systems, adapting them to internal deployment, observability, and release requirements.
• Traffic & Release Safety: Enable shadow serving, canary rollouts, staged deployments, candidate‑versus‑incumbent comparisons, and fast rollback mechanisms for model‑backed services.
• Benchmarking & Evaluation Infrastructure: Build tooling to measure latency, throughput, cost, saturation behavior, and reliability under realistic production traffic.
• Artifact Lifecycle: Improve how model and capability artifacts are packaged, versioned, promoted, deployed, and rolled back across environments.
• Observability & Debuggability: Strengthen runtime telemetry, structured logging, tracing, dashboards, and alerting so engineers can understand model‑serving behavior in production.
• Efficiency & Scale: Contribute to strategies that improve compute efficiency, GPU utilization, autoscaling behavior, and cost‑performance tradeoffs across the inference platform.

Skills you’ll bring

• Production Engineering Experience: 6+ years of professional software engineering experience, with a track record of shipping backend services, infrastructure systems, or production platforms that matter.
• Strong Software Fundamentals: Proficiency in writing maintainable production code in Python, Go, or another backend‑oriented language, with strong debugging and systems‑thinking skills.
• Inference or Systems Orientation: Experience building, operating, or optimizing high‑throughput services, distributed systems, data/ML infrastructure, or runtime platforms where latency, reliability, and resource utilization matter.
• Kubernetes & Linux Fluency: Hands‑on experience with containers, Kubernetes, Linux environments, CI/CD, deployment automation, and production operations.
• Operational Judgment: A strong instinct for reproducibility, observability, rollout safety, failure modes, and whole‑system resilience.
• Performance Awareness: Comfort reasoning about bottlenecks across compute, memory, network, storage, batching, concurrency, and service‑level objectives.
• Collaboration: Ability to work closely with model developers, product engineers, infrastructure teams, and technical leadership to turn evolving AI capabilities into reliable production systems.

Why Join Dialpad

• Work at the center of the AI transformation in business communications
• Build and ship agentic AI products that are redefining how companies operate
• Join a team where AI amplifies every employee’s impact
• Competitive salary, comprehensive benefits, and real opportunities for growth

We believe in investing in our people. Dialpad offers competitive benefits and perks, cutting‑edge AI tools, and a robust training program that help you reach your full potential. We have designed our offices to be inclusive, offering a vibrant environment to cultivate collaboration and connection. Our exceptional culture, repeatedly recognized as a Great Place to Work, ensures that every employee feels valued and empowered to contribute to our collective success.

Dialpad is an equal‑opportunity employer. We are dedicated to creating a community of inclusion and an environment free from discrimination or harassment.