Top Machine Learning Engineer Jobs

Design and implement infrastructure to support generative AI and machine learning, optimizing workflows and integrating new technologies while ensuring reliability and performance.

Responsible for software integration and quality for Cerebras AI platform, focusing on automation, debugging, and cross-team collaboration. Develops QA strategies and testing methodologies to ensure product quality.

The ML Data Engineer develops and maintains machine learning pipelines and systems, collaborating with teams to optimize manufacturing processes through data-driven insights and predictive models.

Research and develop ML and statistical algorithms for networking, edge computing, cybersecurity, and autonomy. Build and evaluate simulation-driven solutions, lead subsystem software development, create automated tests, and contribute to proposals and technical plans while presenting results to customers and senior staff.

Apply machine learning and AI models to real-world problems by designing, building, and deploying software systems. Implement end-to-end model workflows, ingest and transform large datasets, research and test advanced topics, verify software meets requirements, follow QA processes, document findings, and support engineering teams.

Lead research, design, and implementation of classical and ML/DL computer vision algorithms for defect detection, classification, metrology, and image analysis on semiconductor inspection tools. Prototype and evaluate approaches, run feasibility studies and benchmarking on large datasets, optimize performance for available compute (GPU), write production-quality code, and collaborate with software, systems, and compute infrastructure teams to productize solutions.

Build and scale ML development infrastructure and Agentic AI tooling. Optimize distributed training (PyTorch/DDP), improve GPU utilization, design multi-cloud Kubernetes environments, and collaborate with ML researchers and engineers to streamline the ML lifecycle.

Lead development of internal Python SDKs and agentic AI workflows, build scalable FastAPI ML services, maintain CI/CD with Jenkins/GitLab Runners, and use Galileo for model evaluation and observability to productionize autonomous systems.

Build and maintain ML infrastructure for training, serving, and monitoring models for Ads and Recommendations. Improve training reliability and serving performance, own core ML infra components (pipelines, feature/embedding platform), address production issues, and collaborate with ML engineers to deliver robust model pipelines.

Lead end-to-end applied ML: turn problems into datasets, experiments, and production models. Build Python production systems for data collection, feature extraction, scoring, APIs, and observability. Improve LLM systems, handle untrusted data safely, evaluate models and backtests, and work directly with stakeholders to prioritize and iterate.

Own CV accuracy end-to-end: measure and report metrics, investigate misclassifications, curate and label datasets, prioritize retraining, and build continuous learning pipelines. Translate findings into actionable fixes and acceptance criteria, collaborate with ML/CV engineers and customer teams, and eventually implement tooling and model retraining to reduce recurring error patterns in production.

Design, build, and deploy enterprise generative AI solutions using LLMs, RAG, vector search, OCR, and AI agents. Develop cloud-native, Kubernetes-based services, REST APIs, and integrations; contribute to CI/CD, IaC, and architecture reviews; mentor team members and ensure security, observability, and software quality.

Design, build, and operate large-scale ML infrastructure and GPU compute clusters for computer vision and multi-modal models. Own end-to-end pipelines from data ingestion and training to low-latency cloud deployment, orchestration, monitoring, and model performance evaluation. Collaborate with research and product teams to productionize models across warehouse environments.

Design, develop, and deploy production-grade ML and LLM solutions (RAG, agents). Build scalable data pipelines, architect ML/AI systems and MLOps, ensure security/compliance, and engage stakeholders to translate mission needs into AI roadmaps.

Design and build end-to-end AI-native solutions including backend agent orchestration and production-ready frontends. Develop GenAI systems using LLMs, autonomous agents, vector search, and RAG. Rapidly prototype MVPs, deploy cloud-native architectures, and create accessible web interfaces and dashboards for non-technical users.

Design, deploy, and maintain large-scale ML workflows and knowledge graph services. Build production-ready LLM and RAG-based search pipelines, data ingestion/preprocessing, feature stores, monitoring, and security. Collaborate with cross-functional teams, document systems, research advanced ML/NLP techniques, optimize knowledge graph algorithms, and mentor junior engineers.

Build and operate secure, scalable ML pipelines and hybrid GPU infrastructure for training, fine-tuning, serving, and governing open-source LLMs and agentic apps. Implement CI/CD, model versioning, observability, benchmarking, security controls, and integration with RAG/agent tools and vector DBs to support production deployments across tenants.

Design, train, evaluate, and optimize agent-native LLMs and RAG pipelines for enterprise use. Build training and RL pipelines (RLHF/DPO/PPO), embedding-based memory, evaluation harnesses, observability, and inference optimization across cloud and on-prem environments.

Design and build machine-learning-driven robotic systems that operate in the real world, support portfolio company growth, influence product and technical direction, and advance company and career outcomes.

Senior Machine Learning Engineers will develop ML products and lead projects end-to-end—from initial concept through final launch—across S32 portfolio companies. Candidates should drive product direction, have strong ML engineering and productization experience, and make significant business impact.

Design, build, and deploy ML systems that link extracted document features to repayment and fraud outcomes. Run backtests, build evaluation and QA frameworks, develop adaptive/online learning for fraud and credit risk, and collaborate with customers to integrate models into underwriting workflows.

The Machine Learning Engineer will design and implement ML pipelines and productionize models, collaborating with scientists and engineers on scalable systems.

As a Senior Machine Learning Research Engineer, you will research and implement advanced audio ML models, develop inference algorithms, and collaborate with the Operations team to enhance model quality.

As a Senior Machine Learning Engineer, you'll design and implement large-scale E2E driving models, enhance data workflows, and collaborate with cross-functional teams to advance autonomous driving technologies.

Build scalable ML data pipelines for Waabi's autonomous driving platform. Design, optimize, and manage datasets and training processes while collaborating with scientists and engineers.