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.
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.
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.
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.
Our AI Resume Optimizer can help you tailor your resume’s content, section by section, for each of these specific roles.
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.
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.
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.
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.
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.
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.
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:
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?”
