Interested in this AI/ML Engineer role at Duolingo?
Apply Now →Skills & Technologies
About This Role
Our mission at Duolingo is to develop the best education in the world and make it universally available. It's a big mission, and that's where you come in!
At Duolingo, you'll join a team that cares about educating our users, experimenting with big ideas, making fact\-based decisions, and finding innovative solutions to complex problems. You'll have limitless learning opportunities and daily collaborations with world\-class minds — while doing work that's both meaningful and fun.
Join our life\-changing mission to develop education for our half a billion (and growing!) learners around the world.
Read our blog to learn more.
About the role...
Our mission at Duolingo is to develop the best education in the world and make it universally available. With over 500 million users and rapid expansion across new subjects and markets, Duolingo's growth trajectory demands world\-class analytical leadership. We're hiring a Senior Data Science Manager to lead the data science function within our Growth pillar, one of the most consequential areas of the business.
This person will own all aspects of product data science, forecasting, and business intelligence for User Growth. They will lead a team of 6 to 10 data scientists spanning staff to junior levels, with a mandate to coach, develop, and grow their careers while also expanding the team by hiring exceptional talent over time. The ideal candidate combines deep technical fluency in forecasting, statistical/ML modeling, and experimentation with genuine leadership ability: the kind of person who raises the bar for everyone around them.
This role reports to the Head of Data Science \& Analytics and partners closely with cross\-functional leadership in Engineering, Product, Design, and Finance.
You will...
- Set the analytical vision and roadmap for User Growth data science, ensuring the team's work is tightly aligned with company\-level priorities and pillar\-level goals.
- Lead a team of 6 to 10 data scientists. Build a culture of professional growth, intellectual rigor, creative problem\-solving, and shipping impact. Grow the team over time by attracting and hiring outstanding talent.
- Support and evolve the practice of embedding Data Scientists within product development teams, simultaneously maintaining strong stakeholder connection and function cohesion.
- Own the design, execution, and interpretation of experiments and analyses that drive decisions on user acquisition, activation, retention, and reengagement. Improve and advocate for the methodological standards that ensure we learn reliably from every test.
- Develop and maintain user growth forecasting systems. Partner with Finance and Product to deliver credible, timely forecasts of key growth metrics and ensure leadership has the quantitative foundation to set targets and allocate resources.
- Drive business intelligence strategy within Growth, including the metrics layer, self\-serve dashboards, and the analytical infrastructure that enables others to operate with data fluency.
- Translate complex analytical findings into clear narratives for senior leadership and cross\-functional partners. Influence strategic decisions through a combination of technical depth and effective communication.
- Collaborate across organizational boundaries with Product Management, Engineering, Design, Marketing, and Finance to elevate every phase of product development.
✅ You have...
- Experience using data science and analytics to solve product and business problems.
- Experience managing 5\+ person data science teams.
- A graduate degree in Data Science, Statistics, Economics, or a related quantitative field, or equivalent demonstrated expertise in formal analytical techniques.
- Applied industry experience using causal inference, experimentation, and/or machine learning models. You routinely use analytical tools in messy real world settings where details, confounds, and the validity of assumptions matter.
- Proven ability to build, lead, and grow a high\-performing data science team. You have a track record of hiring well, developing people across levels, managing performance, and creating an environment where talented scientists do their best work.
- Strong command of SQL, Python or R, and data/analytics engineering ecosystems. You do not need to write production pipelines, but you need to go deep when the work requires it.
- Exceptional communication skills, both written and verbal, with the ability to distill complex quantitative work into crisp, actionable insights for diverse audiences.
- Exceptional candidates will have...
- Experience operating at a high\-growth, data\-intensive consumer technology company.
- Experience developing and implementing a long\-range analytical vision that shaped product strategy and business outcomes.
- Demonstrated success in building forecasting systems or attribution models at scale.
- Experience navigating the transition from growth\-stage to scaled public company, including the analytical demands that come with investor\-grade reporting and forecasting.
- Experience with AI and machine learning as applied to user behavior modeling, notification optimization, or personalization.
- An impressive Duolingo streak.
Benefits: Take a peek at how we care for our employees' holistic well\-being with our benefits here.
Job Alerts: Sign up for job alerts here.
Accommodations: We will do everything we can within reason to make sure that your interview takes place in an environment that fairly and accurately assesses your skills. If you need assistance or accommodation, please contact [email protected].
Equal Employment Opportunity: Duolingo is proud to be an Equal Employment Opportunity employer. We do not discriminate based upon race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.
Fraud Warning: Unfortunately, there is a rise in scammers pretending to be real Duolingo employees. Duolingo and our employees will never ask for your Social Security number, bank details, or passport info, and we'll never ask you to deposit a check, purchase equipment, or exchange money during the interview process. Real Duolingo employees always use an email that ends in @duolingo.com or @recruiting.duolingo.com. Stay alert and double\-check these details before sharing any information.
By applying for this position your data will be processed as per the Duolingo Applicant Privacy Notice.
Salary Context
This $204K-$306K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Duolingo, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($255K) sits 19% above the category median. Disclosed range: $204K to $306K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Duolingo AI Hiring
Duolingo has 2 open AI roles right now. They're hiring across Research Engineer, AI/ML Engineer. Based in New York, NY, US. Compensation range: $266K - $306K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.