Machine Learning Platform Engineer, Machine Learning, Artificial Intelligence

Parallel Partners

. Build and operate the Machine Learning (ML) infrastructure and platforms powering Artificial Intelligence (AI) products .

Posted 9/22/2026full-timeRemote • California • United States, MaliMid-LevelSenior💰 $140,000 - $180,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and optimizing Machine Learning (ML) infrastructure and platforms for Artificial Intelligence (AI) products, ensuring high reliability, scalability, and performance. Proficient in developing data pipelines and model serving systems that support production workloads efficiently.

Highest-signal resume keywords

Machine Learning (ML) ExperienceArtificial Intelligence (AI) ExperiencePython ProficiencyModel Deployment and InferenceDistributed Systems Understanding

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning (ML)Artificial Intelligence (AI)Production Systems DevelopmentModel EvaluationData PipelinesClean Code PracticesPerformance OptimizationModel Serving InfrastructureContinuous ImprovementBenchmarking Infrastructure

Soft Skills

OwnershipExperimentationAdaptability

Tools & Technologies

PyTorchJAXVLLMSGLangTensorRT-LLMCloud InfrastructureGPU InfrastructureVector DatabasesWorkflow OrchestrationPerformance Tooling

Industry Keywords

Model TrainingModel InferenceSystem ReliabilityProduction WorkloadsObservabilityMonitoringAlertingScalabilityLatencyCost Efficiency

Tech Stack

Tools & technologies

CloudDistributed SystemsPythonPyTorch

About the role

Key responsibilities & impact

Build and operate the Machine Learning (ML) infrastructure and platforms powering Artificial Intelligence (AI) products
Design systems for model training, evaluation, deployment, inference, and experimentation
Build and optimize model serving and inference infrastructure for high-throughput and low-latency workloads
Improve reliability, scalability, latency, and cost efficiency of Artificial Intelligence (AI) systems
Develop reliable pipelines for data preparation, training, evaluation, model release, and continuous improvement
Build platforms and tooling that enable Artificial Intelligence (AI) engineers and researchers to experiment, evaluate, and ship models faster
Develop evaluation and benchmarking infrastructure to measure model quality, performance, and regressions
Build production observability, monitoring, tracing, and alerting for Artificial Intelligence (AI)/Machine Learning (ML) workloads
Identify bottlenecks across the Machine Learning (ML) stack and continuously improve system performance
Work closely with Artificial Intelligence (AI) engineers, researchers, and product teams to turn evolving model requirements into production-ready infrastructure
Ensure AI infrastructure reliably supports production workloads at scale
Ensure models can be trained, evaluated, deployed, and improved efficiently
Ensure inference systems deliver strong latency, throughput, reliability, and cost efficiency
Ensure Machine Learning (ML) pipelines are reproducible, observable, maintainable, and robust
Detect and diagnose model and infrastructure regressions quickly
Create reusable Machine Learning (ML) infrastructure platform primitives
Enable the AI stack to evolve rapidly as new models, architectures, and inference techniques emerge

Requirements

What you’ll need

Machine Learning (ML) and Artificial Intelligence (AI) experience are required
Strong software engineering fundamentals and experience building production systems
Experience building Machine Learning (ML) infrastructure, platforms, or production machine learning systems
Experience with model deployment, inference, evaluation, or data pipelines
Strong understanding of distributed systems and system reliability
Ability to write clean, maintainable, production-quality code
Comfortable working in ambiguous, fast-moving environments
Bias toward ownership, experimentation, and continuous improvement
Proficiency with Python, PyTorch, JAX, LLM and ML serving infrastructure such as vLLM, SGLang, or TensorRT-LLM, cloud infrastructure, distributed systems, ML/data pipelines and workflow orchestration, GPU infrastructure and performance tooling, and vector databases and retrieval infrastructure
Must be willing to take a 60 minute coding assessment

Benefits

Comp & perks

Medical insurance
Dental insurance
Vision insurance
Savings Plan Options
PTO