Staff ML Platform Engineer – MLOps

🕒 il y a 4 mois

🌐 États-Unis, Canada – Distant

🇺🇸 États-Unis – Télétravail 🇨🇦 Canada – Télétravail

💵 $172 000 - $215 000 / an

⏰ Temps Plein

🔴 Expert

🏗️ Ingénieur Plateforme

👻 Score fantôme 31%

Ce que ça veut dire : la probabilité que personne ne recrute vraiment pour cette offre.Comment le lire : 60 % ou plus, on pense que c'est une offre fantôme. Un score plus bas est moins inquiétant.Comment on la calcule : ancienneté, republications, postes ouverts vs taille de l'équipe, rapidité de clôture, et si le salaire est indiqué.À noter : une estimation à partir de données publiques, pas un jugement sur l'entreprise. De vraies offres peuvent avoir un score élevé, alors prenez-le comme un indice, pas comme un fait.

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FutureFit AI

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11 - 50 employés

🤖 Intelligence artificielle

🤝 B2B

☁️ SaaS

💰 Seed Round en 2022-03

Artificial Intelligence • B2B • SaaS

FutureFit AI s'associe avec des entreprises du Fortune 500, des gouvernements et des organisations de développement de la main-d'œuvre pour fournir un outil alimenté par l'IA qui agit comme un GPS pour aider les travailleurs à naviguer dans les transitions de carrière, les soutenant depuis l'orientation professionnelle, jusqu'à la reconversion, en passant par le placement professionnel.

Description

• 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

🎯 Exigences

• 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

🏖️ Avantages

• 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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