Machine Learning Platform Engineer

Stack

Responsibilities

• ▹Build and operate the ML infrastructure and platforms powering A1's AI products
• ▹Design systems for model training, evaluation, deployment, inference, and experimentation
• ▹Build and optimize model serving and inference infrastructure for high-throughput, low-latency workloads
• ▹Improve the reliability, scalability, latency, and cost efficiency of AI systems
• ▹Develop reliable pipelines for data preparation, training, evaluation, model release, and continuous improvement
• ▹Build platforms and tooling that let AI engineers and researchers experiment, evaluate, and ship faster
• ▹Develop evaluation and benchmarking infrastructure to measure model quality, performance, and regressions
• ▹Build production observability, monitoring, tracing, and alerting for AI/ML workloads
• ▹Identify bottlenecks across the ML stack and continuously improve system performance
• ▹Work closely with AI engineers, researchers, and product teams to turn model requirements into production infrastructure

Requirements

• ▹Strong software engineering fundamentals and experience building production systems
• ▹Experience building ML infrastructure, platforms, or production ML 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
• ▹Experience with Python
• ▹Experience with PyTorch or JAX
• ▹Familiarity with LLM and ML serving infrastructure such as vLLM, SGLang, or TensorRT-LLM
• ▹Experience with cloud infrastructure
• ▹Experience with distributed systems
• ▹Experience with ML/data pipelines and workflow orchestration
• ▹Experience with GPU infrastructure and performance tooling
• ▹Experience with vector databases and retrieval infrastructure

Soft skills

About the company

A1 is building a proactive, AI-native assistant for everyday users, most of whom today rely on basic, non-AI-native applications such as email, notes, and tasks. The goal is to bring intelligence to conversations, errands, organizing, and workflows with minimal prompting, aiming for roughly a 90 percent reduction in time spent on daily tasks.