Software Engineer – ML Infrastructure

Gray Swan

. Build and maintain infrastructure, services, and deployment workflows enabling ML models to move from experimentation into reliable production environments .

Posted 11/9/2026full-timePittsburgh • Pennsylvania • United StatesMid-LevelSenior💰 $130,000 - $185,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and maintaining production-grade ML infrastructure and services, with a strong focus on automation, scalability, and observability. Proficient in collaborating with ML engineers to translate complex requirements into effective engineering solutions.

Highest-signal resume keywords

Production-Grade Systems DevelopmentPython ProgrammingCloud Infrastructure FamiliarityKubernetes and Docker ExperienceML Workflow Knowledge

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Production-Grade SystemsPythonAPIsML WorkflowsModel ServingDistributed TrainingInference InfrastructureLarge-Scale Evaluation PipelinesAutomation ToolsInternal Developer Tooling

Soft Skills

CollaborationTechnical Translation

Tools & Technologies

KubernetesDockerAWSGCPRayKubeflowMLflowWeights & Biases

Industry Keywords

ML InfrastructureExperiment OrchestrationPerformance MonitoringAI SafetyAI Security

Tech Stack

Tools & technologies

AWSCloudDistributed SystemsDockerGoogle Cloud PlatformKubernetesPythonRay

About the role

Key responsibilities & impact

Build and maintain infrastructure, services, and deployment workflows enabling ML models to move from experimentation into reliable production environments
Develop internal tools and automation for model training, experimentation, evaluation, and iteration
Design and optimize systems supporting model evaluation pipelines, experiment orchestration, reproducibility, and performance monitoring
Partner with ML engineers and researchers to identify infrastructure bottlenecks and accelerate model development and deployment
Support customer-specific technical requirements and policy-driven workflows by translating evolving needs into scalable engineering solutions
Improve the reliability, scalability, and observability of systems powering ML capabilities

Requirements

What you’ll need

Experience building and maintaining production-grade systems, services, or infrastructure
Experience with Python and backend systems, APIs, and infrastructure code
Familiarity with cloud infrastructure, distributed systems, containerization, or platform engineering
Knowledge of ML workflows, including model training, inference, evaluation, or deployment
Comfortable collaborating with ML engineers and researchers to translate complex technical requirements into reliable software
Experience building ML infrastructure, ML platforms, or internal developer tooling
Familiarity with Kubernetes, Docker, and cloud platforms such as AWS or GCP
Experience with ML infrastructure tools such as Ray, Kubeflow, MLflow, or Weights & Biases
Experience with model serving, distributed training, inference infrastructure, or large-scale evaluation pipelines
Exposure to AI safety, AI security, adversarial testing, or model behavior evaluation
Experience translating customer requirements, security constraints, or policy considerations into technical implementations

Benefits

Comp & perks

Performance-based bonus
Meaningful equity package
401k with up to 4% matching
28 days annual leave (vacation + holidays)
Health, dental, and vision coverage
Catered lunches (Pittsburgh office)
Flexible work arrangements
Visa sponsorship available for exceptional candidates