The OpportunityAdobe is looking for a Machine Learning Engineer who will apply AI and machine learning techniques to big-data problems to help Adobe better understand, lead and optimize the experience of its customers. Partnering with the business units, the candidate will be building various products that address challenging business problems through our customers' full lifecycle, from customer analytics to marketing media optimization.
By using statistical and econometric methods, predictive models, experimental design methods, and optimization techniques, the candidate will be working on the research and development of exciting projects like attribution, media mix modeling, budget optimization, personalization, causal analysis, time series analysis.
Ideal candidates will have a strong academic background as well as technical skills including applied statistics, machine learning, analyzing data, and software development. Familiarity with working with large-scale datasets and scalable data handling approaches would be a plus.
What You'll Do - Develop predictive models on large-scale datasets to address various business problems with advanced statistical modeling, machine learning, and analytics techniques.
- Develop and implement scalable and efficient modeling algorithms that can work with large-scale data in production systems
- Collaborate with product management and engineering groups to develop new products and features.
What You Need to Succeed- PhD or MS in Computer Science, Statistics, Electrical Engineering, Applied Math, Operations Research, or a related technical field or equivalent industry experience
- 3+ years of hands-on experience in machine learning engineering or applied data science
- Deep understanding of statistical modeling, machine learning, and deep learning with a track record of taking these methods from experimentation to production
- Proficiency in Python , experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn
- Familiarity with statistical modeling and analysis tools (R, MATLAB, or equivalent)
- Solid experience with relational databases, SQL, and large-scale data pipelines (Spark, Pandas, or similar)
- Experience with the full ML lifecycle: feature engineering, model training, evaluation, deployment, and post-launch monitoring
- Strong analytical and quantitative problem-solving ability , comfortable with ambiguity and capable of driving toward a solution independently
- Excellent communication skills, able to convey technical findings clearly to both engineering and cross-functional stakeholders, strong collaborator and team player
Nice to Have- Experience with LLMs, RAG pipelines, or generative AI
- Familiarity with cloud ML platforms (AWS SageMaker, Azure ML, GCP Vertex AI)
- Experience with A/B testing and Survival/time-to-event modeling
- Knowledge of responsible AI practices (bias detection, model explain-ability)
Expected Pay Range:Our compensation reflects the cost of labor across several U.S. geographic markets, and we pay differently based on those defined markets. The U.S. pay range for this position is $125,600 -- $234,150 annually. Pay within this range varies by work location and may also depend on job-related knowledge, skills, and experience. Your recruiter can share more about the specific salary range for the job location during the hiring process.
In California, the pay range for this position is $161,700 - $234,150
At Adobe, for sales roles starting salaries are expressed as total target compensation (TTC = base + commission), and short-term incentives are in the form of sales commission plans. Non-sales roles starting salaries are expressed as base salary and short-term incentives are in the form of the Annual Incentive Plan (AIP).
In addition, certain roles may be eligible for long-term incentives in the form of a new hire equity award.
