Machine Learning Engineer

• Architect and implement state-of-the-art pipelines for ad creative generation
• Build systems for product, font, logo, and brand asset preservation using techniques such as IP-Adapters and LoRAs
• Build image and video editing capabilities for smart-cropping, inpainting, outpainting, resizing, and style transfer
• Fine-tune large vision-language models to understand effective ad aesthetics
• Build feedback loops using CTR and CVR metrics to fine-tune generation through RLHF/RLPF for ads
• Engineer filtering and safety systems to prevent non-compliant, unsafe, or copyright-infringing content
• Collaborate with Product, Sales, Policy, UX, and Ads ML teams
• Integrate Ads Creative Effectiveness capabilities into the Ad Manager UI and ad delivery path
• Embed brand-safety filters, copyright checks, and human-in-the-loop escalation paths

Staff Machine Learning Engineer, Ads Creative Effectiveness

• Design, train, and evaluate models for prediction, ranking, and recommendation problems in order management
• Contribute across problem framing, data preparation, feature engineering, training, evaluation, and retraining strategy
• Translate product roadmap priorities into well-framed modeling problems with product management
• Profile customer data, tune and validate models, and work with implementation and solution engineering teams
• Build reproducible Python training pipelines and experiment tracking
• Profile, clean, and validate large-scale enterprise SAP data
• Package models and pipelines for deployment
• Define production monitoring, including drift detection, performance regression signals, and data quality checks
• Translate model behavior and operational reality when production issues arise
• Integrate model output into the product stack through documented service interfaces
• Apply responsible AI practices, including bias evaluation, explainability, and careful data handling
• Document models and explain behavior, limitations, and trade-offs to engineers, delivery teams, sales, and customers
• Review peers' modeling work and raise standards for rigor and reproducibility

Senior Machine Learning Engineer

• Support development of computer vision and machine learning algorithms for object detection, classification, localization, and tracking
• Develop real-time automated target recognition capabilities from stationary and mobile sensor platforms
• Perform visual imagery data science to inform data collection, labeling, and selection for deep computer vision ML training
• Train algorithms and validate data selection and algorithm design through purpose-designed experiments
• Analyze and rank ML algorithms by their success probability on new data
• Set objectives, develop models, and define progress metrics
• Select appropriate datasets and data representations
• Analyze data and model errors and develop strategies to overcome them
• Write and test software integrating machine learning algorithms into aircraft and other systems
• Participate across requirements gathering, algorithm design, prototyping, testing, evaluation, validation, and verification
• Provide on-site support for experimentation and fielded systems
• Perform other duties as assigned

• Prepare manufacturing records, quality events, and operational data for training and evaluation datasets
• Investigate data quality, missing values, and relationships between process execution and quality outcomes
• Develop and evaluate predictive models for operational risk scoring, batch-outcome prediction, recurring deviations, and nonconformance risk
• Experiment with text embeddings, clustering, and retrieval methods for semantic search and quality-event analysis
• Develop and test governed language-model applications grounded in computed results and supporting evidence
• Create reproducible evaluations, investigate failure cases, document findings, and apply appropriate validation methods
• Write and test Python services and APIs
• Contribute to training and inference pipelines
• Measure reliability, latency, and computational efficiency
• Document datasets, features, model configurations, experiments, and evaluation results
• Follow practices for version control, testing, customer-data isolation, and secure data handling
• Collaborate with data scientists, ML engineers, and platform engineers under technical mentorship

AI/ML Engineering Intern

• Build and manage high-throughput, real-time audio and text streaming services
• Handle signal processing, session lifecycles, and concurrency management
• Integrate and serve streaming speech recognition and machine translation models
• Develop confidence scoring, human-intervention routing, and real-time corrections
• Architect and scale GPU-accelerated Kubernetes infrastructure
• Implement batching, load balancing, and autoscaling strategies
• Instrument performance, identify bottlenecks, optimize throughput, and reduce latency
• Define audio ingestion and downstream integration APIs and technical contracts
• Collaborate with research, frontend, platform engineering, and product teams

Machine Learning Engineer – Real-Time Speech Translation

• Design and implement enterprise-grade generative AI solutions and comprehensive pipelines
• Build end-to-end systems integrating structured and unstructured data stores, graph databases, indexing strategies, reinforcement learning strategies, embedding models, and LLMs
• Build and optimize production-grade AI agents using low-code and high-code implementations
• Design, train, and deploy machine learning pipelines covering data ingestion, feature engineering, model selection, hyperparameter tuning, and validation
• Apply statistical modeling and exploratory data analysis to complex datasets
• Design feature stores and data preprocessing and transformation workflows
• Implement model monitoring for concept drift, data quality degradation, and performance regression
• Architect and develop large-scale cloud-native AWS Python applications for high performance, low latency, and horizontal scalability
• Implement Terraform Infrastructure as Code and comprehensive unit, integration, end-to-end, performance, and AI-specific testing
• Apply security controls including IAM, least privilege, RBAC, MFA, encryption, key management, and data masking/tokenization
• Design and manage Docker containers with Kubernetes/EKS or ECS orchestration and auto-scaling
• Establish CI/CD practices using GitHub Actions, Jenkins, GitLab CI, or AWS CodePipeline
• Maintain documentation of architectures, data flows, security controls, and operational procedures
• Contribute throughout the full SDLC from requirements gathering and architecture design through deployment and optimization

Senior AI/ML Engineer

• Partner with stakeholders to identify and refine AI/ML use cases and translate business needs into technical solutions
• Design, build, fine-tune, and evaluate ML and GenAI models, including LLMs, RAG, embeddings, and deep learning, using Vertex AI, Gemini, and open-source tools
• Develop end-to-end ML pipelines covering data ingestion, feature engineering, orchestration, and CI/CD for models and prompts
• Deploy scalable models and agents; manage monitoring, drift detection, and production troubleshooting
• Collaborate with data engineering teams on high-quality data architecture using BigQuery, Dataflow, Pub/Sub, and Feature Store
• Implement Responsible AI, security, governance, and compliance best practices including IAM, encryption, and auditing
• Work cross-functionally with product owners, platform teams, DevOps/SRE, and junior engineers to deliver reliable AI solutions
• Perform experimentation, prototyping, exploratory data analysis, hyperparameter tuning, and documentation of pipelines and workflows

AI/ML 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

• Deploy and productionize machine learning models developed by data science teams
• Build and maintain model deployment pipelines, including packaging, testing, versioning, release management, and rollback processes
• Optimize models and inference services for latency, throughput, scalability, reliability, cost, and resource efficiency
• Support batch, streaming, real-time, and API-based model serving environments
• Partner with data scientists to understand model logic, features, dependencies, validation metrics, and expected production behavior
• Translate model artifacts and technical specifications into production-ready code and services
• Implement monitoring for model performance, data quality, feature drift, model drift, latency, availability, and prediction quality
• Support model validation, A/B testing, champion/challenger testing, and controlled rollout strategies
• Contribute to MLOps capabilities such as CI/CD, model registries, feature stores, orchestration, observability, and automated testing
• Troubleshoot production issues related to model serving, data pipelines, infrastructure, and performance
• Ensure production ML solutions meet requirements for security, compliance, explainability, auditability, and operational resilience
• Work with data scientists, engineers, product teams, and platform teams to move models from experimentation into scalable, reliable, and secure production systems
• Travel 5-10% of the time

Machine Learning Engineer jobs in United States

Staff Machine Learning Engineer, Ads Creative Effectiveness

Senior Machine Learning Engineer