Senior AIML Lead Engineer

Overview

Who we are

To save time applying Toyota does not offer sponsorship of job applicants for employment-based visas or any other work authorization for this position at this time.

Who were looking for

At TFS were building next-generation products that redefine mobility for millions of customers worldwide. Were looking for a Sr Lead Engineer an individual contributor at the principal level who brings deep expertise in machine learning AI systems and large language models combined with the engineering rigor to ship production-grade intelligent systems on AWS.

This isnt a management role. Its for the engineer who sees the signal in the noise: the one who can take a business problem frame it as an ML challenge build the model deploy the pipeline and make it reliable at scale. Youll shape our AI strategy from the ground up work across teams to embed intelligence into our products and mentor engineers who want to grow in this space. If you want to do meaningful applied AI work not just research not just wrappers around APIs this is the role.

This position is based in Plano TX. The selected candidate will be expected to reside within a commutable distance of this location.

Key Responsibilities

• Serve as the technical authority for ML/AI architecture across one or more product domains making high-impact decisions on model selection training strategies inference patterns and tooling
• Design build and maintain end-to-end ML pipelines from data ingestion and feature engineering to model training evaluation deployment and monitoring
• Lead the integration of large language models into production systems including prompt engineering fine-tuning retrieval-augmented generation (RAG) and agent-based architectures
• Evaluate and select the right approach for each problem: foundation models viaAmazon Bedrock custom training onSageMaker classical ML or hybrid approaches
• Lead technical design reviews architecture discussions and RFC processes for AI/ML initiatives driving alignment across engineering teams
• Identify and resolve systemic issues: model drift data quality gaps latency bottlenecks cost inefficiencies and scaling constraints in ML systems
• Define and champion engineering best practices for ML: experiment tracking model versioning reproducibility testing strategies and responsible AI principles
• Collaborate closely with Engineering Managers Product Data Science and Front-End/Backend Engineering to shape roadmaps and ensure technical feasibility of AI-powered features
• Mentor and grow engineers at all levels through code reviews pairing design feedback and technical guidance on ML/AI topics
• Contribute to hiring by conducting technical interviews and helping define what great looks like for ML/AI engineering at TFS
• Proactively communicate technical risks tradeoffs and recommendations to both engineering and non-technical stakeholders

What you bring

• Bachelors degree in Computer Science Machine Learning Statistics or related field or equivalent practical experience
• 7 years of software engineering experience including 35 years focused specifically on ML/AI in production with a track record of operating at a principal or staff engineer level
• Deep understanding ofmachine learning fundamentals: supervised and unsupervised learning deep learning architectures (transformers CNNs RNNs) optimization techniques and evaluation methodologies
• Hands-on experience withlarge language models: prompt engineering fine-tuning (LoRA QLoRA) RAG pipelines embedding models vector databases and agent frameworks (LangChain LlamaIndex or similar)
• Production experience withAWS AI/ML services including:
  • Amazon Bedrockfor foundation model access fine-tuning and knowledge bases or
  • Amazon SageMakerfor custom model training hosting and MLOps pipelines
  • LambdaandStep Functionsfor orchestrating inference workflows
  • S3for data lakes and model artifact storage
  • EventBridgeSQS orSNSfor event-driven ML pipelines
  • OpenSearchor similar for vector search and semantic retrieval
• Strong proficiency inPythonorTypescript you write production-quality ML code not just notebooks
• Experience with core ML frameworks:PyTorchTensorFlow orJAX and libraries like Hugging Face Transformers scikit-learn and XGBoost
• Solid understanding ofMLOps practices: experiment tracking (MLflow W&B) model registries CI/CD for ML A/B testing and canary deployments for models
• Experience withdata engineeringfundamentals: ETL pipelines feature stores data validation and working with structured and unstructured data at scale
• Strong understanding ofInfrastructure as CodeusingAWS CDK CloudFormation or Terraform for ML infrastructure
• Experience withobservability and monitoringfor ML systems: model performance tracking data drift detection and alerting
• Deep experience debugging complex issues across ML systems from training instabilities to inference latency to data pipeline failures
• Strong written and verbal communication you can write a clear RFC lead a design review and explain model tradeoffs to a non-technical stakeholder

Added bonus if you have

• Masters or PhD in Machine Learning AI Computer Science Statistics or related field
• Experience in the financial services banking or insurance industry
• Experience withresponsible AI: fairness metrics bias detection explainability (SHAP LIME) and model governance frameworks
• Familiarity withcomputer visionorNLPbeyond LLMs (named entity recognition document understanding OCR)
• Experience withreal-time inferenceat scale: model optimization (quantization distillation ONNX) GPU/accelerator management and latency-sensitive serving
• Experience withmulti-modal modelsand architectures that combine text image and structured data
• Hands-on experience withGraphQL federationor API gateway patterns for exposing ML services
• Experience withcontainerized ML workloads(ECS Fargate Docker Kubernetes) for training and serving
• AWS certifications (Machine Learning Specialty Solutions Architect Developer Associate)
• Published research or conference presentations in ML/AI
• Experience contributing to or maintaining open-source ML projects
• Experience defining engineering standards writing ADRs or leading org-wide technical initiatives

What well bring
During your interview process our team can fill you in on all the details of our industry-leading benefits and career development opportunities. A few highlights
include:

• A work environment built on teamwork flexibility and respect
• Professional growth and development programs to help advance your career as well as tuition reimbursement
• Team Member Vehicle Purchase Discount
• Toyota Team Member Lease Vehicle Program (if applicable)
• Comprehensive health care and wellness plans for your entire family
• Toyota 401(k) Savings Plan featuring a company match as well as an annual retirement contribution from Toyota regardless of whether you contribute
• Paid holidays and paid time off
• Referral services related to prenatal services adoption childcare schools and more
• Tax-Advantaged Accounts (Health Savings Account Health Care FSA Dependent Care FSA)

Belonging at Toyota

Our success begins and ends with our people. We embrace all perspectives and value unique human experiences. Respect for all is our North Star. Toyota is proud to have 10 different Business Partnering Groups across 100 different North American chapter locations that support team members efforts to dream do and grow without questioning that they belong.

Applicants for our positions are considered without regard to race ethnicity national origin sex sexual orientation gender identity or expression age disability religion military or veteran status or any other characteristics protected by law.

Have a question need assistance with your application or do you require any special accommodations Please send an email to .