Machine Learning Platform Engineer, Machine Learning (ML) and Artificial Intelligence (AI) Required, Work From Home

Parallel Partners•San Francisco, CA

18h•$140,000 - $180,000•Remote

About The Position

As the Machine Learning Platform Engineer, you will build the infrastructure and systems that power Artificial Intelligence (AI) capabilities. You will design and operate the systems behind the Artificial Intelligence (AI) stack, from model training and evaluation to deployment, inference, observability, and continuous improvement. You will work closely with Artificial Intelligence (AI) engineers, researchers, and product engineers to turn models into reliable, scalable, and cost-efficient production systems. You will build the platforms, tooling, and infrastructure that enable the team to experiment quickly and bring AI capabilities to production with confidence. Machine Learning (ML) and Artificial Intelligence (AI) experience are required. This position is 100% Remote.

Requirements

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.
Python
PyTorch
JAX
LLM and ML serving infrastructure such as vLLM, SGLang, or TensorRT-LLM
Cloud infrastructure
Distributed systems
Machine Learning (ML)/data pipelines and workflow orchestration
GPU infrastructure and performance tooling
Vector databases and retrieval infrastructure

Responsibilities

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.
Improve Artificial Intelligence (AI) systems across reliability, scalability, latency, throughput, and cost.
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.