Software Engineer – Machine Learning Infrastructure

Salesforce

. Design, build, and operate systems to train, serve, and deploy machine learning models at scale, with a focus on reliability, performance, and operational simplicity .

Posted 4/24/2026full-timeSeattle • Texas, Washington • 🇺🇸 United StatesMid-LevelSenior💰 $148,500 - $313,700 per yearWebsite

Tech Stack

Tools & technologies

AirflowAWSAzureCloudDistributed SystemsGoogle Cloud PlatformKubernetesRaySpark

About the role

Key responsibilities & impact

Design, build, and operate systems to train, serve, and deploy machine learning models at scale, with a focus on reliability, performance, and operational simplicity
Evolve GPU backed inference infrastructure to support high throughput, latency sensitive workloads, including large scale model serving
Architect and optimize distributed training and data processing systems using platforms such as Ray, Airflow, Spark, or similar technologies
Build and maintain Kubernetes based platforms and orchestration layers using tools such as KubeRay, vLLM, and internally developed services
Architect solutions that bridge legacy systems with modern technologies while maintaining monolithic application stability
Develop robust monitoring, observability, and alerting for production ML workloads to ensure operational excellence
Partner closely with AI Platform, ML modeling, security, and product engineering teams to design infrastructure that supports evolving AI use cases
Provide technical leadership through design reviews, mentorship, and by setting engineering standards and long term architectural direction for ML infrastructure
Author technical design and architecture documentation, and contribute thought leadership through engineering blog posts

Requirements

What you’ll need

Significant professional experience in software engineering with a strong focus on infrastructure, backend systems, platform engineering, or MLOps
Deep experience building and operating distributed systems, including expert level knowledge of Kubernetes and container based platforms
Hands on experience with modern ML infrastructure and serving stacks such as Ray or KubeRay, vLLM, or similar training and inference orchestration frameworks
Experience working with GPU infrastructure, including performance optimization and operational management at scale
Strong experience with data infrastructure and orchestration technologies such as Airflow, Spark, or similar systems
Experience building and operating cloud native systems on public cloud platforms such as AWS, GCP, or Azure, including infrastructure as code
A demonstrated ability to drive technical direction for complex systems and balance short term delivery with long term architectural goals
Excellent written communication, as well as ability to thrive in an asynchronous and globally distributed infrastructure team.
A related technical degree required

Benefits

Comp & perks

time off programs
medical, dental, vision
mental health support
paid parental leave
life and disability insurance
401(k)
employee stock purchasing program

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learningdistributed systemsinfrastructurebackend systemsplatform engineeringMLOpsperformance optimizationdata processingmonitoringobservability

Soft Skills

technical leadershipmentorshipwritten communicationcollaborationdesign reviews

Certifications

related technical degree