object detection jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-10-29, object detection appears in 375 job postings indexed by Skillenai over the past 90 days — Machine Learning Engineer has the most postings mentioning object detection, with demand share up 5.0% vs the prior 4 weeks.
Last updated · 90d ending 2026-10-29
Which roles want object detection?
Upload your resume and Skillenai will show which roles your object detection experience fits, which skills you already cover, and what is missing.
Frequently asked questions about object detection
+Is object detection in demand in 2026?
Yes. object detection appears in 375 job postings indexed by Skillenai over the 90 days ending 2026-10-29. Machine Learning Engineer accounts for the most postings mentioning object detection (20.0% of all postings mentioning object detection).
+What jobs require object detection?
According to the Skillenai jobs index over the 90 days ending 2026-10-29, among roles with at least 20 postings, the highest shares mentioning object detection are Perception Engineer (44.4% of that role’s postings mention object detection), Algorithm Engineer (32.5% of that role’s postings mention object detection), Computer Vision Engineer (31.2% of that role’s postings mention object detection).
+What skills are commonly paired with object detection?
Across job postings indexed by Skillenai (90 days ending 2026-10-29), object detection most often appears alongside Python, Computer Vision, PyTorch, deep learning, Machine Learning.
+Where is object detection most in demand?
As of 2026-10-29, the metro areas posting the most jobs requiring object detection are San Francisco, Singapore, Sunnyvale, Chantilly, Long Beach, according to the Skillenai jobs index.
+How can I keep up with new object detection content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning object detection alongside the jobs index. You can subscribe to a daily email digest of new object detection content from your Skillenai account.
+Which skills come before and after object detection?
The skill-flow chart shows skills documented in adjacent positions across observed employer changes. An outgoing skill is documented in the following position but not the preceding one. These are ideas to explore, not proven prerequisites, acquisition dates, or levels of mastery. Each ribbon counts employer moves with that skill pair; one move can contribute several pairs.
Weekly indexed postings requiring object detection — last 90 days
Career paths around object detection
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before object detection
Skills after object detection
How to read this chart · view counts
Each side is an independent set of observed employer moves, not the same people followed through three stages. Ribbon widths compare move counts within that side. Internal moves are not included.
The following position documents a skill that the preceding position does not. Skills must be linked to both positions, with clear dates and no overlap. One move can connect several skill pairs. These patterns suggest skills to explore; they do not establish prerequisites, when a skill was learned, or a higher skill level.
Source: Skillenai talent graph, historical career profiles. Historical descriptions and coverage can change. Only the leading published connections are shown.
| Connection | Moves |
|---|---|
| Before: python | 18 |
| Before: sql | 7 |
| Before: scikit-learn | 4 |
| Before: machine learning | 4 |
| Before: xgboost | 4 |
| Before: NLP | 3 |
| Before: ETL pipelines | 3 |
| Before: computer vision | 3 |
| After: python | 6 |
| After: computer vision | 6 |
| After: sql | 5 |
| After: machine learning | 4 |
| After: Tableau | 3 |
| After: deep learning | 3 |
| After: MongoDB | 3 |
| After: Java | 3 |
Roles most likely to require object detection
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Perception Engineer | 12 | 44.4% |
| Algorithm Engineer | 13 | 32.5% |
| Computer Vision Engineer | 30 | 31.2% |
| Deep Learning Engineer | 5 | 16.7% |
| Autonomy Engineer | 3 | 13.6% |
| Robotics Software Engineer | 8 | 5.2% |
| FPGA Engineer | 1 | 5.0% |
| AI Research Engineer | 3 | 4.3% |
| AI Lead | 1 | 3.7% |
| Senior Data Scientist | 1 | 3.7% |
Roles with the most object detection postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Machine Learning Engineer | 75 | 20.0% |
| ML Engineer | 31 | 8.3% |
| Computer Vision Engineer | 30 | 8.0% |
| Software Engineer | 23 | 6.1% |
| Data Scientist | 21 | 5.6% |
| AI Engineer | 14 | 3.7% |
| Algorithm Engineer | 13 | 3.5% |
| Perception Engineer | 12 | 3.2% |
| Research Scientist | 11 | 2.9% |
| AI/ML Engineer | 10 | 2.7% |
Top companies posting jobs requiring object detection
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| RocketHub | 22 |
| Bosch | 18 |
| CLERA | 14 |
| Skydio | 12 |
| Snap | 8 |
| True Anomaly | 7 |
| Torcrobotics | 6 |
| Saronic Technologies | 6 |
| Axon | 5 |
| Motional | 5 |
Job postings indexed over the past 90 days, grouped by resolved employer. Counts are postings, not hires. Companies without a published page appear without a link.
Top metros hiring for object detection
| Name | Postings | Share |
|---|---|---|
| San Francisco | 21 | 5.6% |
| Singapore | 14 | 3.7% |
| Sunnyvale | 12 | 3.2% |
| Chantilly | 10 | 2.7% |
| Long Beach | 10 | 2.7% |
| San Mateo | 10 | 2.7% |
| Austin | 9 | 2.4% |
| Tucson | 9 | 2.4% |
| Seattle | 8 | 2.1% |
Skills commonly paired with object detection
Explore related pages
How this was computed
Weekly job-count charts omit the incomplete current week (weeks start Monday, UTC). Totals include all postings in the stated window. Counts derive from the Skillenai jobs index over the 90 days ending 2026-10-29. Skills are resolved against the Skillenai canonical taxonomy, so the same entity is counted whether a posting writes 'Python', 'Python 3', or 'python'. Role prevalence divides postings mentioning object detection by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s object detection postings by all object detection postings, including postings without a role. Shares need not sum to 100% for the displayed roles. Pages refresh weekly (or daily for the top-50 most-requested skills). Adjusted posting share: 0.1% to 0.1%. Demand share change is the relative percentage change between these adjusted shares. Each employer-and-ATS group has at least 10 postings in each 90-day window; its earlier posting count supplies the same weight in both windows. The panel includes 2,482 identified employers and covers 71% of earlier and 70% of latest indexed postings. Windows: 2026-06-30 to 2026-09-28 and 2026-07-28 to 2026-10-26 (UTC; end dates excluded). The windows overlap by 62 days. Dates reflect indexing, not the employer’s posting date. This measures posting mix, not total hiring or market-wide demand. Matching excludes entrants and exits; changes in crawl completeness within an employer or ATS can still affect the result.
- source
- Skillenai jobs index, deduplicated daily
- entity_id
- 788511c1e72502f7
- data_as_of
- 2026-10-29
- window_days
- 90