Who Can Apply
Many positions accept candidates with strong machine learning and computer vision experience. Employers differ on international hiring — some accept remote applicants worldwide while others require local work authorization. Always verify the 'work authorization' and 'location' fields on the original job posting.
Job Summary
| Position | Data Scientist (Computer Vision) |
| Posted On | 03 December 2025 |
| Location | Remote / US / Europe / Multiple locations (see individual listing) |
| Employment | Full-Time / Contract (varies by employer) |
| Experience | 2–6 years preferred (mid-senior level) |
Qualifications Required
| Qualification | Requirement |
|---|---|
| Programming | Strong Python experience; reproducible ML pipelines (Jupyter, scripting). |
| Deep Learning | Experience with PyTorch or TensorFlow; training CNNs, Transformers for vision tasks. |
| Computer Vision | Object detection, segmentation, image preprocessing, augmentation techniques. |
| Data Handling | Working with large annotated datasets; familiarity with COCO/Pascal/Label formats. |
| Deployment | Knowledge of model serving, Docker, cloud inference (AWS/GCP/Azure) preferred. |
Salary & Benefits (Guidance)
- Salary range (approx): USD $80,000–$150,000 per year (varies by location & experience).
- Contract roles may pay hourly or fixed project fees.
- Benefits: healthcare (varies), paid time off, learning budget, remote equipment allowances.
Official Application Links
| Company | Position | Location | Official Apply Link |
|---|---|---|---|
| Amazon | Senior Applied Scientist – Computer Vision, Camera & Sensors | Seattle, USA | Apply Now |
| Amazon | Applied Scientist – Computer Vision, International Machine Learning | Seattle, USA | Apply Now |
| Amazon | Senior C++ Computer Vision Engineer, Camera & Sensor Software | Seattle, USA | Apply Now |
| WhatsApp Alerts | Daily Updates | Join Channel |
Responsibilities
- Design, train and evaluate computer vision models for production use.
- Create and maintain annotated datasets, data pipelines and augmentation strategies.
- Prototype and deploy models using Docker, REST APIs, or cloud-based serving.
- Work with cross-functional teams to integrate vision capabilities into products.
- Monitor model performance and iterate on improvements and robustness.
Career Path
- Machine Learning Engineer → Computer Vision Engineer → Senior CV Researcher
- Lead Data Scientist → Head of AI / ML → Director of Applied Research
Documents to Prepare
- Resume (PDF) with links to GitHub or portfolio showing CV projects.
- Reproducible code examples or notebooks demonstrating model results.
- Public dataset experiments, model cards or short video demos (optional).
- Relevant certifications or degree documents (if required by employer).
Work Authorization & Visa Notes
We do not guarantee visa sponsorship. Some employers may sponsor eligible candidates; many roles are remote and do not require relocation. Always verify details on the employer's official posting. Avoid listings that make unsupported claims about guaranteed sponsorship.
Tips to Improve Selection
- Include a short project in your portfolio that reproduces a CV benchmark (e.g., fine-tune a detector on a custom dataset).
- Provide a clear README and inference instructions for code samples.
- Write an ATS-friendly CV and an employer-specific cover note focusing on relevant tasks.
- Show end-to-end understanding — data collection → model training → deployment → monitoring.
Challenges to Prepare For
- High competition from experienced ML engineers.
- Need to demonstrate production readiness, not just academic accuracy.
- Time-zone coordination when working with remote teams across continents.
How to Advance in CV
- Contribute to open-source CV projects and publish reproducible experiments.
- Attend workshops, follow recent CV conferences (CVPR, ICCV, NeurIPS papers).
- Gain experience in model optimization, quantization and on-device inference.
FAQs
Some employers accept international or remote applicants. Check each job's original posting for exact eligibility. We avoid claiming guaranteed sponsorship.
Highlight relevant projects, include code links, show performance metrics and explain deployment experience. Short video demos or notebooks help.
Use reputable aggregators: RemoteRocketship, Arc.dev, Remote100K, and company careers pages. Always click through to the source posting.