Senior Machine Learning Engineer, ML Infrastructure

Unity

. Design and operate large-scale online inference infrastructure serving production ML models with low latency and high reliability .

Posted 10/7/2026full-timeRemote • Washington • United StatesSenior💰 $165,600 - $273,400 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and operating production-grade online ML inference systems, with a strong focus on optimizing model performance and ensuring system reliability. Proficient in leveraging tools and frameworks for distributed training and model serving, while maintaining observability and cost efficiency.

Highest-signal resume keywords

Production-Grade Online ML Inference SystemsModel Serving FrameworksDistributed SystemsPython ProgrammingModel Deployment Workflows

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Applicant Tracking System Keywords

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Hard Skills

Model CompilationDynamic BatchingGPU AccelerationQuantizationKubernetesService ReliabilityModel PackagingArtifact ValidationA/B ExperimentationRuntime Tuning

Soft Skills

Systems ThinkingTechnical Leadership

Tools & Technologies

PyTorchRay DataRay TrainNVIDIA Triton Inference ServerTorchServeRay ServeTensorFlow ServingFlyteAirflow

Industry Keywords

ML InfrastructureModel Serving WorkflowsObservabilityScalabilityCost Efficiency

Tech Stack

Tools & technologies

AirflowDistributed SystemsKubernetesPythonPyTorchRayTensorflow

About the role

Key responsibilities & impact

Design and operate large-scale online inference infrastructure serving production ML models with low latency and high reliability
Develop infrastructure supporting distributed training workflows using PyTorch, Ray Data, and Ray Train
Integrate ML pipelines with workflow orchestration systems such as Flyte or Airflow
Optimize model performance through model compilation, GPU/CPU utilization improvements, request scheduling, kernel fusion, and runtime-level tuning
Improve ML systems observability through latency, throughput, error-rate, cost, saturation, and model-health monitoring
Partner with ML engineers to support faster model iteration while maintaining production safety, scalability, and cost efficiency
Improve reliability and reproducibility of model serving workflows, including model packaging, artifact validation, compatibility testing, and deployment automation
Lead architectural improvements to make the online ML platform more robust, user-friendly, scalable, and cost-efficient

Requirements

What you’ll need

Experience building and operating production-grade online ML inference systems
Experience with model serving frameworks such as NVIDIA Triton Inference Server, TorchServe, Ray Serve, TensorFlow Serving, or similar systems
Experience optimizing inference workloads using dynamic batching, model compilation, quantization, GPU acceleration, GPU kernel optimization, caching, or runtime tuning
Strong experience with distributed systems, Kubernetes, autoscaling, service reliability, and production observability
Strong programming skills in Python, with practical experience working on production ML systems and high-scale services
Experience with PyTorch and modern model deployment workflows, including model packaging, validation, and serving lifecycle management
Experience designing infrastructure for safe model rollout, canary testing, A/B experimentation, and automated rollback
Strong systems thinking and ability to reason about latency, throughput, reliability, scalability, and cost tradeoffs
Proven ability to lead technical direction and influence architectural decisions across teams without formal authority
Sufficient knowledge of English for professional verbal and written exchanges

Benefits

Comp & perks

Equity awards
Participation in company incentive plans, such as annual discretionary bonuses or sales commissions
Comprehensive health, life, and disability insurance
Commute subsidy
Employee stock ownership
Competitive retirement/pension plans
Generous vacation and personal days
Leave and family-care programs for new parents
Office food snacks
Mental Health and Wellbeing programs and support
Employee Resource Groups
Global Employee Assistance Program
Training and development programs
Volunteering and donation matching program