Databricks Has 69 AI Jobs Right Now—Most Candidates Don't Know What They Are
There are two AI employers in the index that have posted 60+ open AI/ML roles for months without much candidate attention: OpenAI, which everyone applies to, and Databricks, which most people forget is a company you can work at.
Databricks sits at #2 in HireIndex’s August 3, 2026 index with 69 open AI/ML roles — just behind OpenAI (92) and ahead of Scale AI (59), Amazon (50), and everyone else. The company has held between 65 and 69 roles for four consecutive weeks. It is not a blip or a hiring surge. It is a structural part of the AI hiring market that most job search conversations skip entirely.
How many Databricks AI jobs are open right now?
69, in the week of August 3, 2026. Here’s where that sits relative to the rest of the index (1,623 total open AI/ML roles across 765 companies):
| Company | Open AI/ML Roles | 4-Week Trend |
|---|---|---|
| OpenAI | 92 | 90 → 85 → 93 → 92 |
| Databricks | 69 | 69 → 65 → 69 → 69 |
| Scale AI | 59 | 45 → 47 → 52 → 59 |
| Amazon | 50 | 47 → 51 → 56 → 50 |
| 39 | 37 → 39 → 40 → 39 | |
| Capital One | 36 | 49 → 49 → 45 → 36 |
The stability in Databricks’ count stands out. OpenAI fluctuates week to week. Scale AI is actively expanding. Databricks has been essentially the same for a month — consistently high, not accelerating and not dropping. That pattern matches a company in steady hiring mode across multiple functions, not one running a targeted campaign.
What types of roles does Databricks actually post?
This is where the expectations gap gets wide. Looking at all 69 open roles, the largest categories are not ML Engineer or Research Scientist:
Customer-facing and go-to-market roles account for roughly half the count. The specific titles: Solutions Architect, Specialist Solutions Architect, Technical Solutions Engineer, Forward Deployed Engineer, Delivery Solutions Architect, Account Executive (including a “Frontier AI Lab Account Executive” role), Customer Enablement Manager, and Partner Engineer. These are roles for engineers and technical professionals who work directly with enterprise customers to deploy Databricks’ platform — not roles for people building the platform itself.
The engineering-track roles are real and technically serious. Staff Software Engineer on AI Runtime, AI Native Web Platform, Foundation Model Inference, and AI Search. Staff Machine Learning Engineer. Senior Machine Learning Engineer on the GenAI Platform. Senior Software Engineer on AI/ML Environments. Staff Backend Engineer on AI Platform. These are production infrastructure roles for someone who wants to work on the systems that run AI workloads at enterprise scale.
The research function is small but genuine. Principal Research Scientist — Scaling, a Principal AI Research Scientist/Research Director role on AI Scaling, and a PhD GenAI Research Scientist internship. Three research-tagged roles out of 69 total. Databricks does publish ML research — the DBRX model, contributions to inference optimization, work on data curation for pretraining — but open research headcount is thin compared to what OpenAI or Scale AI post.
Product and design round out the count. Staff Product Manager (Agentic AI Applications, AI Platform), Senior Product Manager (Databricks AI), Principal Product Marketing Manager (AI Governance), Staff Product Designer (AI Products). These are product-track careers at a company with a real AI product suite.
Where are Databricks roles located?
Every open role in the index is listed as remote. That is not a rough characterization — it is the literal result for all 69 positions. Databricks does have offices (San Francisco, New York, Amsterdam), but the open positions are posted with remote eligibility, which makes them accessible to candidates outside major tech hubs in a way that OpenAI or Scale AI positions are not.
For comparison: OpenAI’s 92 roles are heavily weighted toward San Francisco. Scale AI’s 59 roles include a large remote portion but also concentrate in specific locations. Databricks is the largest employer in the index where remote is the default for the entire open position set.
What seniority does Databricks hire at?
Senior and above, almost exclusively. Here is the breakdown for the 69 current roles:
| Seniority | Count | Share |
|---|---|---|
| Junior / Entry-level | 1 | 1% |
| Mid-level | 15 | 22% |
| Senior | 25 | 36% |
| Staff / Principal | 25 | 36% |
| Director / Leadership | 3 | 4% |
That single junior role is a PhD research internship — not a full-time position. For practical purposes, Databricks has no open entry-level headcount. The seniority skew is heavier than the overall index average and reflects two things: enterprise customer-facing roles require experienced technical professionals who can run complex deployments independently, and the core platform engineering roles are Staff and Principal-level positions building systems at significant scale.
Scale AI, for contrast, has 39 of 59 open roles at mid-level. The two companies are both in the top three by role count, but they are hiring for different experience profiles. Scale AI is an option for engineers 2-4 years into their careers. Databricks, at current postings, is not.
What does this mean for candidates?
The clearest path into Databricks’ AI/ML market is through the Solutions Architect and Forward Deployed Engineer tracks, which account for the largest share of open roles and are actively distributed across the index. These roles require technical depth in data engineering, ML deployment, and enterprise software — not ML research backgrounds. Candidates with experience shipping data pipelines or managing ML infrastructure at scale, combined with some customer-facing work, fit the profile better than candidates coming from academic ML or frontier lab backgrounds.
The Machine Learning Engineer and AI Platform roles that do exist at Databricks are serious. Staff-level titles at a company that runs significant AI workloads means working on production-scale inference, retrieval, and runtime systems. The work is not research, but it is not trivial engineering either.
For candidates who want research and happen to also be interested in Databricks: the three open research roles are targeting principal-level scientists with a specific focus on scaling. That is a narrow opening.
What Databricks is not is an AI lab in the OpenAI or Anthropic sense. It is a data and AI platform company with a large technical go-to-market function, real engineering depth in its core product, and a small research team. All 69 roles are remote. Almost all require 5+ years of experience. The market is larger than most candidates realize and has a completely different shape than what tends to get covered when people write about where to work in AI.