

Machine Learning Engineer (Local to California Only W2 Candidates)
Position: Machine Learning Engineer
Location: San Jose, CA (Onsite)
Responsibilities and Duties:
· 8+ years in machine learning, including 5+ years in reinforcement learning, recommendation systems, pricing algorithms, pattern recognition, or artificial intelligence.
· Expertise in classical ML techniques (e.g., Classification, Clustering, Regression) using algorithms like XGBoost, Random Forest, SVM, and KMeans, with hands-on experience in RL methods such as Contextual Bandits, Q-learning, SARSA, and Bayesian approaches for pricing optimization.
· Proficiency in handling tabular data, including sparsity, cardinality analysis, standardization, and encoding.
· Strong coding skills in Python and SQL (including Window Functions, Group By, Joins, and Partitioning).
· Experience with ML frameworks and libraries such as scikit-learn, TensorFlow, and PyTorch.
· Knowledge of controlled experimentation techniques, including causal A/B testing and multivariate testing.
Key Responsibilities
· Algorithm Development: Conceptualize, design, and implement state-of-the-art ML models for dynamic pricing and personalized recommendations.
· Reinforcement Learning Expertise: Develop and apply RL techniques, including Contextual Bandits, Q-learning, SARSA, and concepts like Thompson Sampling and Bayesian Optimization, to solve pricing and optimization challenges.
· AI Agents for Pricing: Build AI-driven pricing agents that incorporate consumer behavior, demand elasticity, and competitive insights to optimize revenue and conversion.
· Rapid ML Prototyping: Quickly build, test, and iterate on ML prototypes to validate ideas and refine algorithms.
· Feature Engineering: Engineer large-scale consumer behavioral feature stores to support ML models, ensuring scalability and performance.
· Cross-Functional Collaboration: Work closely with Marketing, Product, and Sales teams to ensure solutions align with strategic objectives and deliver measurable impact.
· Controlled Experiments: Design, analyze, and troubleshoot A/B and multivariate tests to validate the effectiveness of models.
Job Types: Full-time, Contract
Pay: $70.00 - $73.00 per hour
Schedule:
8 hour shift
Day shift
Monday to Friday
Ability to Relocate:
San Jose, CA 95110: Relocate before starting work (Required)
Work Location: In person