Machine Learning Staff Engineer (Senior)
Redwood City, CA, United States
PubMatic
PubMatic maximizes customer value to deliver the programmatic digital marketing supply chain of the future and stay ahead of advertising technology trends.Company Description
PubMatic is a digital advertising technology company for premium content creators.
The PubMatic platform empowers independent app developers and publishers to control and maximize their digital advertising businesses.
PubMatic’s publisher-centric approach enables advertisers to maximize ROI by reaching and engaging their target audiences in brand-safe, premium environments across ad formats and devices.
Since 2006, PubMatic has created an efficient, global infrastructure and remains at the forefront of programmatic innovation. Headquartered in Redwood City, California, PubMatic operates 13 offices and nine data centers worldwide.
Job Description
We are immediately hiring a Machine Learning Staff Engineer to join us in Redwood City on a hybrid schedule.
Reporting to the SVP of Addressability & Marketplace in Eastern Time, this senior contributor is a proven 'doer' to develop, implement and extend data-intensive ML software for real-time auctioning, ad inventory estimation, and audience segmentations.
Working with our Big Data, Ad Serving, and Product Managers, you will apply Machine Learning to create POCs (Proofs of Concept). Then you will lead other Data Scientists to implement the POCs into production and scale up the solutions.
Responsibilities:
- Design and implement core components of our algorithms, as well as model the large amounts of data that PubMatic generates daily
- Develop and implement data-intensive machine learning software for real-time auctioning, ad inventory estimation, audience segmentations, and other AdTech applications
- Work with data scientists, product managers, and software engineers to develop and support the software for new Machine Learning products
- Ensure excellence in delivery to internal and external customers
Qualifications
- PhD in a STEM field
- 3+ years of hands-on industry work experience designing and building large-scale ML algorithms and ETL that are well-designed, cleanly coded, well-documented, operationally stable, and timely delivered
- 5+ years total analytical work, including academic research
Solid Experience with a Mix of:
- Python or R, including ML libraries (SKLearn, NumPy, caret, e1071), including CPU/GPU parallelization, matrix algebra, vectorization, linear programming, lambda programming, OOP
- At least one of the DL frameworks (TensorFlow, PyTorch, Caffe, Theano, Keras, or alike)
Understanding of:
- Graduate statistics and probability (inference, hypothesis testing, p-value, ANOVA, CLT, LLN, Bayes’ theorem, A/B testing, combinatorics, PDF/CDF, joint/conditional/marginal densities)
- Vector calculus (gradients, Jacobians, partial derivatives and integrals, optimization)
- Linear algebra (eigen values/vectors, inverses, decompositions, orthogonality, multi-linear)
- Time series (ARIMA, GARCH, forecasting, Kalman filter)
- Shallow ML algorithms: regressions, SVM, kMeans, kNN, NB, HMM, PCA, NMF, SVD, XGBoost, decision trees, ensemble methods (random forest)
- Deep NN algorithms: MLP, RNN, LSTM, CNN, GRU
- ML concepts: backprop, hyperparameter tuning (Bayesian optimization, grid/random search), regularization, learning rate, optimization
- Advanced work with SQL or NoSQL, including nested/join/aggregate queries, stored procedures, over partition by, basic stat functions
- Cloud compute engines (AWS, Azure, GCP and alike), ML on clusters of GPUs, SageMaker, Jupyter
- Excellent communication skills, cultural fit and natural curiosity in learning the ML developments and domain expertise
Nice to Have:
- Experience in Programmatic advertising and RTB
- Deep reinforcement learning (Bellman equations, MDP, policy optimization, credit assignment, or multi-agent)
- Proficiency with Spark (ML Lib, GraphX), Hadoop, Kafka, Hive
- Scala, Java, C/C++
- Record of STEM publications in top journals or conferences
- High rank at Kaggle competitions
#LI-SD1
Additional Information
Return to Office: PubMatic employees around the world have returned to our offices via a hybrid work schedule (3 days “in office” and 2 days “working remotely”) that is intended to maximize collaboration, innovation, and productivity among teams and across functions. All PubMatic employees in the US and India are required to be fully vaccinated to return to our offices. Covid-19 boosters are not required at this point in time.
Benefits: Our benefits package includes the best of what leading organizations provide such as, paid leave programs, paid holidays, healthcare, dental and vision insurance, disability and life insurance, commuter benefits, physical and financial wellness programs, unlimited DTO in the US (that we actually require you to use!), reimbursement for mobile and internet expenses and fully stocked pantries plus in-office catered lunches 3 days per week.
Diversity and Inclusion: PubMatic is proud to be an equal opportunity employer; we don’t just value diversity, we promote and celebrate it. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
Tags: A/B testing AWS Azure Bayesian Big Data C++ Caffe ETL GCP GPU Hadoop Jupyter Kafka Keras Lambda Linear algebra Machine Learning NoSQL NumPy OOP PhD Python PyTorch R Research RNN SageMaker Scala Scikit-learn Spark SQL Statistics STEM TensorFlow Testing Theano XGBoost
Perks/benefits: Career development Conferences Insurance Lunch / meals Wellness
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