Machine Learning Engineer

• Research and prototype advanced machine learning methods for perception, prediction, planning, or decision-making
• Design, train, evaluate, and improve models using large-scale multimodal driving and sensor data
• Build data pipelines, experimentation workflows, and evaluation tools for rapid model iteration
• Validate models through simulation, testing, performance analysis, and failure-case investigation
• Optimize models and systems for scalability, latency, reliability, and deployment constraints
• Collaborate with engineering and research partners to integrate prototypes into autonomous vehicle systems
• Document findings, present technical insights, and contribute to publications or other research outputs

Summer Intern – AI/ML Engineer, Autonomous Vehicles, Simulation

• Leverage AI/ML techniques to scale testing, classify autonomy performance, and mine critical scenarios, integrating human-in-the-loop where appropriate
• Collaborate with engineers to integrate ML models into simulation tests and evaluation pipelines
• Ramp up quickly on the codebase and domain and deliver assigned tasks efficiently
• Develop, test, and deploy production-ready code across simulation infrastructure components
• Define problem statements and implement optimal, tradeoff-aware solutions covering edge cases
• Collaborate with cross-functional stakeholders and contribute to design and code reviews
• Ensure seamless integration through clear communication
• Apply work directly to iteration and validation of GM's self-driving stack

Summer Intern – AI/ML Engineer, Autonomous Vehicle, Simulation

• Develop solutions for high-fidelity risk identification in customer data
• Translate risk identification methods into agent logic
• Ensure quality across risk analysis, explanations, and recommendations
• Collaborate with threat-research teams to understand data, threats, and security heuristics
• Identify and solve data requirements for analysis
• Develop and collaborate on data pipelines, enrichments, and aggregations with data engineering teams
• Apply backtesting, precision/recall balancing, tuning, and quality control to threat detection
• Deploy and monitor solutions in production within the CI/CD framework
• Report to the Manager AI Platform and Data Science in the AI Platform and Data Science department

Senior Staff Machine Learning Engineer

• Develop solutions applying data analysis and threat research, AI, machine learning, and data engineering to automate security analysis
• Translate risk identification methods into agent logic for risk analysis, explanations, and recommendations
• Collaborate with threat-research teams to understand data, threats, and security heuristics
• Identify and solve data requirements for analysis
• Develop and collaborate with data engineering teams on pipelines, enrichments, and aggregations
• Apply data-driven quality methods to threat detection, including backtesting, precision/recall balancing, tuning, and quality control
• Deploy and monitor solutions in production using the CI/CD framework

Senior Machine Learning Engineer

• Architect and guide scalable, production-grade machine learning systems for customer-facing and operational products
• Lead end-to-end ML lifecycle execution, including data sourcing, feature engineering, model training, deployment, and monitoring
• Drive innovation through deep learning, NLP, reinforcement learning, and multimodal modeling approaches
• Mentor engineering teams and influence architectural decisions
• Collaborate cross-functionally to align ML solutions with platform strategies and downstream applications
• Establish standards for model evaluation, fairness, and responsible AI compliance
• Publish technical insights and contribute to Walmart’s engineering knowledge base

Principal Machine Learning Engineer

• Turn research ideas, including transformers, ensemble models, and new detection and classification approaches, into production features from prototype through evaluation, rollout, and iteration
• Design and build perception pipeline components on a stream-processing architecture using Flink/PyFlink
• Work with the platform team on model serving, configuration management, and orchestration
• Own and improve production computer vision models, including training, evaluation, and inference optimization using Triton, TensorRT, and multi-backend serving
• Design logic that converts model outputs into trustworthy incidents through tracking, filtering, probabilistic reasoning, and state machines
• Build observability through metrics, dashboards, and evaluation loops
• Reduce end-to-end latency and cost while maintaining detection quality
• Partner with GTM, customer success, and data operations on customer rollouts, deployment tuning, labeling, and review quality
• Evaluate and integrate third-party ML tooling for orchestration, labeling, and dataset curation
• Drive adoption of ML tooling across the team
• Write design documents and requirements
• De-risk major changes with proofs of concept
• Provide technical leadership through design and execution without people management
• Mentor teammates through design reviews, code reviews, and context sharing

Senior Software Engineer, ML Systems

• Set and evolve the research direction for core intelligence, including context representation, memory, reasoning, planning, and orchestration
• Decide when to design new model architectures versus adapting or leveraging frontier open-source or commercial models
• Define evaluation frameworks measuring real-world usefulness, robustness, safety, and long-term behavior
• Own alignment, safety, and guardrail strategy as first-class product concerns
• Guide exploration of retrieval-augmented training, mixture-of-experts, distillation, multi-agent orchestration, and multimodal systems
• Shape early product intelligence direction in close partnership with product and application engineering
• Set the technical bar for research rigor, judgment, and taste across the organization

VP of Research, Machine Learning, Artificial Intelligence

• Design end-to-end solutions for perception and autonomous vehicle stacks to enable traffic signal detection across diverse driving environments, including complex intersections, rural roads, and highways
• Conduct applied research and development of innovative deep learning models for traffic light detection, traffic sign recognition, road marking detection, construction object detection, text recognition, and other traffic signal tasks
• Develop generalizable approaches to support diverse ODDs and country/region expansion
• Drive and prioritize data-driven development with large data collection and labeling teams
• Plan data collection and labeling priorities and optimize labeling efficiency to maximize data value
• Leverage data simulation and augmentation to solve extreme scenarios
• Productize perception solutions by meeting product requirements for safety, latency, and software robustness

Senior Machine Learning Engineer, Perception – Autonomous Driving

• Design and implement models that dynamically reorder or highlight channels within the PlutoTV EPG
• Design models to support content scheduling decisions based on user preferences and content affinity
• Deliver production-ready TensorFlow and PyTorch code within GCP infrastructure
• Identify and mitigate issues related to live data streams and real-time scheduling constraints
• Guide junior engineers on Linear TV data and FAST-specific success metrics
• Lead design of complex scheduling and ranking features
• Personalize the PlutoTV Linear/FAST experience, including channel presentation, scheduling, and guide personalization

• Develop and maintain machine learning models integral to the production decision-making system and business outcomes
• Adopt or build technologies for training and serving ML models
• Monitor the latest ML research for applicable ideas
• Build and scale efficient ML pipelines for real-time and batch processing
• Track model performance, detect drift, and automate retraining
• Explain statistical and machine learning concepts to technical and non-technical audiences
• Collaborate with world-class engineers
• Contribute to engineering excellence through advanced tools, risk-driven testing, explainable systems, and code review