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Tracking AI hiring across 200+ US tech companies. Stage, salary, and stack signals on every role — refreshed weekly.

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Currently tracking 1 active AI role, down 52% versus the prior 4 weeks. Primary focus: Post-train · Engineering. Salary range $221k–$331k (avg $276k).

Hiring
1 / 1
Momentum (4w)
↓-12 -52%
11 opens last 4w · 23 prior 4w
Salary range · avg $276k
$221k–$331k
USD · disclosed roles only
Tracked since
6w ago
last role 3w ago
Hiring velocityscroll left for older weeks
1 new role
Jan 27
2 new roles
Oct 6
1 new role
13
1 new role
27
1 new role
Nov 3
1 new role
Dec 1
1 new role
8
1 new role
Jan 5
2 new roles
12
4 new roles
19
4 new roles
26
8 new roles
Feb 2
1 new role
9
1 new role
16
2 new roles
23
10 new roles
Mar 2
4 new roles
9
4 new roles
16
5 new roles
23
6 new roles
30
2 new roles
Apr 6
4 new roles
13
8 new roles
20
9 new roles
27
2 new roles
May 4
4 new roles
11
3 new roles
18
2 new roles
25
2 new roles
Jun 1

Frequently asked questions

  • What AI roles is Duolingo hiring for?

    Duolingo currently has 1 active AI-related role in our index. The most common open titles are: Senior Product Manager, DET. Most positions are in Product.

  • What stage of AI development does Duolingo focus on?

    Duolingo's active AI hiring is concentrated in: application (100%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.

  • Where is Duolingo hiring AI talent?

    Duolingo is hiring AI talent in: United States (1 role).

  • What technologies does Duolingo's AI team work with?

    Job postings at Duolingo most frequently reference: evals.

  • How many AI roles has Duolingo posted recently?

    In the past 30 days, Duolingo has posted 0 new AI-related roles.

Jobs (1)

0 AI · 56 total active
Show
Active onlyAI only (≥ 7)
Stage
AllPost-train · 1
Function
AllEngineering · 1
Sort
AI scoreRecentTitle
TitleStageFunctionLocationFirst seenAI score
Staff AI Research Engineer
Staff AI Research Engineer at Duolingo focused on the Monetization team, developing and deploying AI systems including bandit models and large language models to improve the learning experience and drive business objectives. The role involves full-stack ML development from feature engineering and training data to fine-tuning, reinforcement learning, evaluation, deployment, and monitoring.
Post-trainAgentEngineeringPittsburgh, PA6w ago8