10 Remote Computer Vision Jobs Paying Up to $335,300
Today’s List at a Glance A hand-picked list of top-tier roles for ambitious professionals. Here’s the breakdown: 💰 Salary Range: $72,000 – $335,300 a year 🏢 Top Companies Hiring: General Motors, Cisco Systems, Hugging Face 📍 Geographic Spread: 10 remote positions (roles listed as Remote, US-Anywhere Remote, or Remote-in-city such as Mountain View and Seattle) 🪜 Seniority Level: Focus on senior and staff-level technical roles (Staff, Senior, Machine Learning Engineer II / Data Scientist II) Featured Machine Learning & Computer Vision Roles Staff Software Engineer at Indeed 📍 Location: Remote 💰 Salary: $143,000 – $207,000 a year Why it’s a great opportunity: High-paying remote ML platform engineering role at a major job-tech company building scalable model lifecycle tooling—ideal for engineers who want to support production-ready computer vision models. View Job Post → AI Engineer II (Remote – US) at BNSF Railway 📍 Location: Remote (US) 💰 Salary: $123,750 – $175,000 a year Why it’s a great opportunity: Strong mid/senior-level remote AI engineering role with a clear six-figure compensation band—great for ML engineers applying computer vision and predictive models to large-scale operational data. View Job Post → Staff AI / ML Engineer – Embodied AI at General Motors 📍 Location: Remote in Mountain View, CA 💰 Salary: $218,800 – $335,300 a year Why it’s a great opportunity: Top-compensated remote role focused on embodied AI and robotics—an excellent fit for CV engineers wanting to work on perception systems for autonomous and robotic platforms. View Job Post → Staff Machine Learning Engineering (Remote) at Cisco Systems 📍 Location: Remote in Seattle, WA 💰 Salary: $193,800 – $317,100 a year Why it’s a great opportunity: Enterprise-scale ML staff role with a very competitive pay range and emphasis on running deep-learning inference at scale—well suited for CV engineers focused on productionizing models. View Job Post → Our AI Resume Optimizer can help you tailor your resume’s content, section by section, for each of these specific roles. Optimize Your Resume Now Senior Open-Source Machine Learning Engineer, Computer Vision – US Remote at Hugging Face 📍 Location: US Remote 💰 Salary: $130,000 – $180,000 a year Why it’s a great opportunity: Directly CV-focused remote role at Hugging Face with solid compensation—perfect for engineers who want to improve open-source vision tooling and production CV models. View Job Post → Data Scientist II – Computer Vision at Socure Inc 📍 Location: US-Anywhere Remote 💰 Salary: $100,000 – $130,000 a year Why it’s a great opportunity: Remote computer-vision scientist role with a clear salary band—good entry point for ML engineers specializing in document/image verification and CV model development. View Job Post → ML/AI Data Engineer (Remote) at FEI Systems 📍 Location: US-Anywhere Remote 💰 Salary: $90,000 – $130,000 a year Why it’s a great opportunity: Remote ML/data engineering role with explicit pay that supports ML pipelines—attractive for CV engineers who want to own data infrastructure and preprocessing for vision models. View Job Post → AI/ML Engineer – Python/GenAI/.Net/C# – Remote at UnitedHealthGroup 📍 Location: US-Anywhere Remote 💰 Salary: $72,000 – $130,000 a year Why it’s a great opportunity: Remote AI/ML engineering role at a major healthcare company with a listed salary range—appealing for CV engineers looking to apply vision and ML to healthcare data and workflows. View Job Post → AI/ML Platform Engineer (Remote Opportunity) at Vetsez 📍 Location: Remote 💰 Salary: $100,000 – $130,000 a year Why it’s a great opportunity: Remote platform-focused ML role with a clear compensation band—valuable for CV engineers aiming to build and maintain platforms that serve vision models in production. View Job Post → ML Engineer – Generative AI & LLMs (Remote) at Ample Insight Inc 📍 Location: Remote 💰 Salary: $90,000 – $130,000 a year Why it’s a great opportunity: Remote ML engineering role with a stated salary that, while LLM-focused, offers transferable large-scale model engineering experience useful for senior computer vision engineers expanding into multimodal systems. View Job Post → Strategic Playbook for Landing These Roles 🎯 Profile of an Ideal Candidate Core Responsibility: Design, build, and productionize scalable computer vision and ML systems, and deliver the platform and tooling required to run inference reliably at scale. Essential Experience: A strong background in deep learning and computer vision combined with hands-on experience deploying models to production, building ML pipelines, or developing perception systems for robotics/autonomy. Key Competencies: Beyond technical prowess, these roles demand systems thinking, cross-functional leadership, clear stakeholder communication, and a track record of owning end-to-end ML projects. 📄 The Resume Blueprint: Keywords & Metrics Keywords to Target: Computer Vision Model Lifecycle Inference at Scale ML Platform Deep Learning Metrics that Matter: ✅ Reduced inference latency by 40% through model optimization and improved serving architecture, enabling 2x higher throughput for real-time CV pipelines. ✅ Deployed models to production across X services (e.g., 3 products or 5 microservices), maintaining 99%+ uptime and automated CI/CD for model rollouts. ✅ Improved accuracy / reduced error by Y percentage points on a critical vision task by implementing data augmentation, synthetic data, and model ensemble strategies, cutting false positives/negatives materially. 💬 Nailing the Narrative: Your Interview Strategy Be prepared to answer tough, strategic questions. Here are some specific examples: “Describe a time you took a research-stage CV model to production. What were the biggest engineering trade-offs you made, and how did you measure success post-deployment?” “Walk us through a system design for serving low-latency perception models on edge devices—how would you balance model size, accuracy, and throughput?” “Tell us about a cross-functional initiative you led where ML performance metrics conflicted with product or business goals. How did you resolve it?” 💡 Pro Tip: Structure answers with context → action → results: quantify latency/accuracy gains, explain trade-offs, and cite monitoring/alerting you implemented to prove reliability in production. 🚀 Put Your Playbook into Action Sign Up for Free!
