Develop and deploy production machine learning models for content classification and segmentation. Build multi-class and multi-label classifiers, perform exploratory analysis, feature engineering, model training, tuning, and evaluation, and create text- and metadata-based features using Python, SQL, pandas, and Spark. Develop scalable workflows, maintain tested and documented code, and collaborate with data scientists, engineers, and stakeholders.
City: Santa Monica, CA & Seattle, WA
Onsite / Hybrid / Remote: Hybrid. Onsite Monday through Thursday. Remote Friday.
Duration: 18 months
Rate Range: Up to $103.50/hr on w2
Work Authorization: GC, USC, All valid EADs except OPT, CPT, H1B
Must Have:
- 5+ years of hands-on industry experience developing and deploying machine learning models in production.
- Strong Python programming skills, including pandas and scikit-learn.
- Hands-on experience with supervised machine learning, particularly classification models.
- Experience with Natural Language Processing (NLP), including text-based feature development.
- Strong experience with feature engineering, preprocessing, and model evaluation.
- Strong SQL skills for querying and analyzing large datasets.
Responsibilities
- Design, develop, and improve machine learning models for content classification and segmentation.
- Build classification models that automatically assign content categories, genres, and segments.
- Enhance existing machine learning models and expand classification coverage across different content types.
- Develop multi-class and multi-label classification models using supervised learning techniques.
- Perform exploratory data analysis, feature engineering, model training, tuning, and evaluation.
- Process structured and semi-structured datasets using Python, pandas, SQL, and Spark.
- Develop text-based and metadata-driven features to improve classification accuracy.
- Apply techniques such as categorical encoding, normalization, feature selection, and hyperparameter optimization.
- Build scalable machine learning workflows to support production applications.
- Collaborate with data scientists, software engineers, and business stakeholders to implement ML solutions.
- Maintain clean, modular, tested, and documented machine learning code.
Qualifications:
- Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field, or equivalent practical experience.
- Minimum 5 years of relevant machine learning experience, with substantial hands-on model development.
- Strong understanding of classification algorithms, including logistic regression, decision trees, random forests, ensemble methods, and gradient-boosted models.
- Experience building, training, evaluating, and deploying machine learning classifiers.
- Experience with multi-class and/or multi-label classification.
- Experience with NLP techniques and text-derived features.
- Strong knowledge of cross-validation, model performance metrics, and hyperparameter tuning.
- Ability to independently deliver machine learning solutions from initial data exploration through production implementation.
- Experience with software development practices, including Git, testing, logging, and code reviews.
Nice to Have
- Master's degree in Computer Science, Machine Learning, Statistics, Mathematics, or a related field.
- Experience in media, entertainment, streaming, or digital content platforms.
- Experience building classifiers using content metadata, catalog data, or high-dimensional datasets.
- Experience with NLP techniques such as TF-IDF, text embeddings, and semantic representations.
- Experience with workflow orchestration tools such as Airflow or Luigi.
- Experience with AWS or similar cloud environments.
- Experience with search, recommendation systems, or content categorization.
aKube Inc Los Angeles, California, USA Office
Los Angeles, California, United States
Similar Jobs
.jpeg)
Artificial Intelligence • Hardware • Information Technology • Machine Learning
Implement advanced HBM SoC logic and base-die designs from netlist to GDSII. Responsibilities include floorplanning, placement, clock-tree synthesis, routing, timing closure, IP integration, physical signoff, power integrity, DFT coordination, tape-out execution, post-silicon analysis, and workflow automation. The engineer collaborates with RTL, verification, packaging, manufacturing, and other cross-functional teams while improving PPA and enabling AI-driven design workflows.
Top Skills:
Ai-Enabled WorkflowsCadence InnovusCadence TempusCpfDftDrcEdaErcGdsiiLvsMbistMmmcSdcSiemens CalibreStaSynopsys Icc2Synopsys PrimetimeUpf
Artificial Intelligence • Hardware • Information Technology • Machine Learning
Develop and lead firmware and embedded software for next-generation custom HBM products. Responsibilities include analyzing requirements, architecting and implementing driver, validation, manufacturing, test, and production firmware, using FPGA prototyping, supporting silicon bring-up and debug, and collaborating across hardware, RTL, verification, architecture, and customer teams. The role requires expertise in memory technologies, low-level firmware, high-speed interfaces, reliability features, AI datacenter systems, and AI-assisted development workflows.
Top Skills:
Ai/MlDramEmbedded SoftwareFirmwareFpgaGenaiHbmJedec StandardsMemory ControllersNandPhyRtlSoc
Blockchain • Fintech • Payments • Financial Services • Cryptocurrency • Web3
Build and evaluate fraud models for onboarding, login, account changes, and money movement. Develop labels, features, sampling strategies, thresholds, and production monitoring to detect account takeover, scams, money mules, and identity fraud. Transition third-party risk models to in-house solutions, measure model performance and drift, collaborate with fraud, risk, operations, and compliance teams, and evaluate AI agent workflows for transaction review and fraud-ring analysis.
Top Skills:
Ai AgentsBlockchain AnalyticsLightgbmPythonSQLXgboost
What you need to know about the Los Angeles Tech Scene
Los Angeles is a global leader in entertainment, so it’s no surprise that many of the biggest players in streaming, digital media and game development call the city home. But the city boasts plenty of non-entertainment innovation as well, with tech companies spanning verticals like AI, fintech, e-commerce and biotech. With major universities like Caltech, UCLA, USC and the nearby UC Irvine, the city has a steady supply of top-flight tech and engineering talent — not counting the graduates flocking to Los Angeles from across the world to enjoy its beaches, culture and year-round temperate climate.
Key Facts About Los Angeles Tech
- Number of Tech Workers: 375,800; 5.5% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Snap, Netflix, SpaceX, Disney, Google
- Key Industries: Artificial intelligence, adtech, media, software, game development
- Funding Landscape: $11.6 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Strong Ventures, Fifth Wall, Upfront Ventures, Mucker Capital, Kittyhawk Ventures
- Research Centers and Universities: California Institute of Technology, UCLA, University of Southern California, UC Irvine, Pepperdine, California Institute for Immunology and Immunotherapy, Center for Quantum Science and Engineering
