Growth Machine Learning Engineer (Remote) $150K+ | ClickUp AI Jobs

📍 Location: ca

🏷 Type: Not specified

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Job Overview

This Mid-to-Senior level Machine Learning Engineering role at ClickUp sits at the core of a fast-scaling AI-powered productivity platform. You will own the full lifecycle of production ML systems, from feature pipelines to deployment and monitoring. The ideal candidate has 4+ years of experience, strong expertise in MLOps, cloud infrastructure, and scalable ML systems, and thrives in high-impact environments where experimentation directly influences product growth.

🌍 Work Eligibility & Location

This role is structured as Remote but geographically constrained to North America, which limits global accessibility. While visa sponsorship is possible in select cases, it is not guaranteed, meaning international candidates without work authorization face a higher barrier. Candidates already based in the US or Canada have a clear advantage in the hiring pipeline.

💰 Salary Intelligence

This compensation sits in the top tier for mid-level ML engineers and aligns with high-growth SaaS companies. The addition of equity, bonuses, and full benefits increases total compensation significantly. For remote ML roles, this range is competitive, especially for candidates without FAANG-level experience but with strong MLOps and production system exposure.

📊 Role Breakdown

This role is heavily execution-focused with a strong balance between engineering rigor and data-driven experimentation. Approximately 40% of your time will be spent on building and deploying ML models using frameworks like TensorFlow and PyTorch, ensuring low latency and high availability. Another 30% is dedicated to MLOps infrastructure, including pipelines with MLflow, Kubeflow, or SageMaker, and optimizing CI/CD workflows.

The remaining 30% focuses on feature engineering, monitoring, and collaboration. You will design scalable feature pipelines, ensure data quality, and implement model drift detection systems. A key differentiator is your ability to bridge data science and engineering teams, translating experimental models into production-grade systems. This is not a research-heavy role — it is deeply focused on shipping reliable ML systems at scale.

🧩 Required Skills & Fit

📈 Difficulty & Competitiveness

This is a high-difficulty role due to its expectation of end-to-end ownership across ML lifecycle, infrastructure, and deployment. Candidates with only modeling experience but lacking production deployment exposure will struggle. The 4+ years requirement is realistic, but top candidates often exceed it with strong portfolios. Competition is intense due to the remote nature and strong employer brand.

🚀 Career Impact

This role offers strong career acceleration for engineers aiming to specialize in production AI systems. You will gain hands-on experience with scalable ML infrastructure, which is one of the most in-demand skills globally. The exposure to real-world product impact positions you for senior and staff-level roles or transitions into AI platform leadership.

📋 Key Responsibilities

You will design, build, and deploy scalable ML systems using Python and frameworks like PyTorch. A core responsibility is to develop and maintain MLOps pipelines using tools such as MLflow and Kubeflow, ensuring seamless training, deployment, and monitoring. You will collaborate with data scientists to productionize models and optimize feature pipelines for performance and reliability.

Additionally, you will implement monitoring systems to track model performance, detect drift, and trigger retraining workflows. You are expected to optimize inference speed, improve system scalability, and ensure integration with broader product architecture. This role requires continuous performance tuning and cross-functional collaboration.

🎯 Application Strategy

🧠 Application Optimization (Adaptive)

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🧠 Fit & Positioning Analysis

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📅 Application Signals

This role shows moderate urgency typical of high-impact engineering hires. Given the remote setup and strong salary band, competition is high and early applicants gain a visibility advantage. Candidates with demonstrable production ML experience and deployed systems will move faster through screening.

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✅ Job Source & Verification

This job listing is sourced directly from the official ClickUp careers platform, ensuring high source credibility and accurate role details. Compensation, responsibilities, and requirements reflect the original posting. Last updated: April 2026. Candidates should verify application links and avoid third-party intermediaries.