Senior Machine Learning Engineer

At Toyota Research Institute (TRI), we’re on a mission to improve the quality of human life. We’re developing new tools and capabilities to amplify the human experience. To lead this transformative shift in mobility, we’ve built a world-class team advancing the state of the art in AI, robotics, driving, and material sciences.

The Team 

Research Software Engineering (RSE) is part of TRI's Technical Engineering & Operations (TEO) organization. RSE teams are embedded engineers who bridge the gap between cutting-edge research and production deployment. We build the infrastructure, tooling, and platforms that keep TRI's researchers productive, and we translate research breakthroughs into robust, scalable systems that can be incorporated into Toyota products.

The Opportunity

We're looking for a Senior Machine Learning Engineer to partner with research teams across TRI's robotics, autonomous vehicles, energy, and materials science programs. You'll work at the intersection of ML infrastructure and applied AI — building systems that accelerate research velocity, and translating experimental work into production-quality deployments.

The ideal candidate is a strong generalist who can move fluently across the ML stack, from cloud training infrastructure to LLM integrations to data pipelines. We also value candidates who bring deep expertise in two or more specific areas — for example, someone who combines strong MLOps fundamentals with hands-on edge/embedded ML experience, or who pairs LLM systems expertise with robust data engineering skills.

This is a high-impact, cross-cutting role. Your work directly enables breakthrough research across TRI's programs.

Build and maintain machine learning infrastructure, including training pipelines, distributed compute systems, model serving platforms, and monitoring tools that research teams rely on daily.

Integrate and evaluate large language models (LLMs) and foundation models by developing retrieval-augmented generation (RAG) systems, performing full and adapter-based fine-tuning, applying prompt engineering techniques, and benchmarking performance across providers such as AWS Bedrock, Gemini, Claude, GPT, and open-source models.

Design scalable data pipelines to support multimodal data, including text, images, sensor data, speech, video, and structured scientific datasets.

Consult with research teams to understand machine learning requirements, evaluate potential approaches, and propose solutions aligned with TRI’s technology stack and engineering standards.

Support edge and embedded machine learning by optimizing, quantizing, and deploying models to onboard hardware platforms such as robotics systems and vehicles.

Bridge the gap between research and production by translating experimental notebooks and prototypes into maintainable, scalable, and deployable systems while preserving research innovation.

Stay current with advancements in machine learning and artificial intelligence by evaluating emerging techniques and assessing their potential adoption within TRI.

Drive technical quality by participating in code reviews, producing clear documentation, and fostering knowledge sharing across the Research Software Engineering (RSE) team.

Candidates should have one of the following: a BS with 6–10 years, an MS with 5–9 years, a PhD with 3–7 years, or no degree with 9–13 years of equivalent experience; specific degree fields are flexible, with demonstrated experience prioritized over pedigree.

Strong proficiency in PyTorch and/or TensorFlow, with hands-on experience building, fine-tuning (including adapter-based methods such as LoRA and QLoRA), evaluating, and deploying large language models.

Experience working with multimodal data—including text, images, sensor/telemetry data, and speech—and understanding the associated data characteristics and pipeline requirements.

Proven track record of deploying and maintaining machine learning systems in production environments.

Experience with AWS services for machine learning workloads (e.g., Bedrock, SageMaker, ECS/Batch, S3), strong Python fundamentals, and comfort working within polyglot codebases.

Ability to consult effectively with researchers, translate ambiguous technical requirements into actionable solutions, operate autonomously on cross-team problems, and communicate clearly in both written and verbal contexts.

Experience optimizing models for resource-constrained hardware through quantization, pruning, and compilation frameworks (e.g., TFLite, LiteRT, ONNX), along with proficiency in C/C++ and/or CUDA for performance-critical inference.

Familiarity with MLOps practices such as experiment tracking (MLflow, Weights & Biases), CI/CD for ML, and model versioning (e.g., DVC), as well as containerization (Docker required; ECS/Batch preferred; Kubernetes a plus), distributed training across multi-GPU and multi-node setups, and experience with Vertex AI in addition to AWS.

Background in robotics, autonomous systems, materials science, or energy domains, with experience translating published research into production systems (“paper-to-production”), working in academic or industry R&D environments, and developing agentic AI systems with tool use and multi-step reasoning.

AWS certifications (e.g., Solutions Architect, ML Specialty) and contributions to open-source machine learning projects.

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