Staff ML Platform Engineer – MLOps

🕒 Junho 8

🌐 Estados Unidos, Canadá – Remoto

🇺🇸 Estados Unidos – Remoto (EUA) 🇨🇦 Canadá – Remoto

💵 $172.000 - $215.000 / ano

⏰ Tempo Integral

🔴 Especialista

🏗️ Engenheiro de Plataforma

👻 Score fantasma 32%

O que significa: a chance de ninguém estar realmente contratando para esta vaga.Como ler: 60% ou mais significa que achamos que é uma vaga fantasma. Pontuações mais baixas preocupam menos.Como pontuamos: tempo em aberto, republicações, vagas abertas vs tamanho do time, rapidez para fechar vagas, e se o salário está divulgado.Observação: é uma estimativa de dados públicos, não um julgamento da empresa. Vagas reais podem ter pontuação alta, então use como uma dica, não como um fato.

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☁️ SaaS

💰 Seed Round em 2022-03

Artificial Intelligence • B2B • SaaS

A FutureFit AI faz parceria com empresas da Fortune 500, governos e organizações de desenvolvimento da força de trabalho para fornecer uma ferramenta impulsionada por inteligência artificial que atua como um GPS para ajudar trabalhadores a navegar em transições de carreira, apoiando-os desde a orientação profissional, passando pelo requalificação, até a inserção no mercado de trabalho.

Descrição

• Build and own the ML platform supporting ML and LLM-powered products • Evaluate current pipelines, data architecture, and ML workflows and create a prioritized improvement plan • Provision compute and manage reproducible training and serving environments • Maintain frameworks and packages across services and model images • Optimize latency, throughput, and infrastructure spend without destabilizing production • Build LLM infrastructure with cost-aware routing across models and providers • Implement prompt and response evaluation to validate routed-model quality • Establish model experimentation practices including shadow deployments, canaries, holdouts, and success criteria • Implement model and data monitoring, alerting, regression detection, lineage, and traceability • Operate and debug production ML systems, including models built by others • Own feature computation, storage, and consistent serving between training and inference • Establish deployment and monitoring standards for shared team ownership • Collaborate with the data and engineering team and mentor team members as needed

🎯 Requisitos

• Staff-level, hands-on experience in MLOps, ML platform, or ML infrastructure • Experience standing up MLOps practices end to end, including model CI/CD, experiment tracking, model registries, deployment workflows, and monitoring • Production experience with LLM-based systems, including serving, prompt and response evaluation, routing across models and providers, and cost and latency management • Experience operating models in batch and real-time serving contexts • Hands-on experience with containers, compute provisioning, reproducible environments, and package/framework maintenance • Experience with A/B tests, shadow or canary deployments, holdouts, and pre-defined success criteria • Expertise in production ML observability and traceability, including drift/regression detection, alerting, lineage, and prediction reproduction • Hands-on operational experience debugging production ML incidents and fixing systems not originally built by the candidate • Comfort with data engineering, feature computation/storage, and training-serving feature consistency • Track record improving complex, fast-grown systems • Strong systems design ability and ability to build architecture directly • Ability to work in EST/EDT business hours • Legally eligible to work in Canada or the United States • Willingness to undergo a background check if required • Willingness to travel occasionally for offsites and team gatherings

🏖️ Benefícios

• Remote-first work arrangement • Optional access to the Toronto office • Approximately 2-3 trips per year, including the company off-site • Reasonable accommodation for disabilities • Equal opportunity and inclusive workplace

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