Top Machine Learning Engineer Jobs

Design and implement end-to-end AI agent systems, collaborate with cross-functional teams, and oversee deployment and performance evaluation in production environments.

As a Senior Machine Learning Engineer, develop LLM evaluation frameworks and agentic AI workflows to extract insights from conversations and optimize customer experiences.

As a Senior ML Ops Engineer, you lead deployment and operational management of ML models, ensuring platform resilience, automation of CI/CD processes, and observability while collaborating with various engineering teams.

The Physical Design Engineer will lead efforts in solving physical design challenges, improve RTL-to-GDS methodologies, and integrate AI/ML solutions into CAD flows, focusing on performance and runtime improvements.

Lead QA strategy and automation for ML/AI-powered services. Develop LLM-assisted test generation, build scalable automation frameworks, validate ML/LLM outputs, debug model and service issues, and integrate testing into CI/CD for distributed systems.

As a Senior Machine Learning Engineer, you will drive AI vision, develop models, and collaborate cross-functionally to enhance user experiences.

Design, implement, and operate scalable ML Runtime microservices and developer tooling. Build infrastructure-as-code, CI/CD pipelines, container orchestration, monitoring, and automation on public cloud. Collaborate with ML teams, provide on-call support, and drive platform reliability and developer self-service.

Lead design and maintenance of speech-focused test suites, evaluate and optimize ASR/TTS/speech-to-speech models across languages, produce quantitative benchmark reports, and recommend model selection and deployment optimizations for production.

As a Senior Machine Learning Engineer at Strider Technologies, you'll design and maintain ML solutions for document processing and classification, working with AWS and NLP techniques to enhance operational efficiency and decision-making.

Drive end-to-end ML solutions for Uber Freight: identify opportunities, research techniques, prototype, deploy and monitor production models at scale, and integrate Generative AI/agentic systems to optimize logistics and operations.

Design, build, and maintain scalable ML infrastructure and data pipelines for training, deployment, monitoring, and evaluation of large-scale models (including LLMs). Build APIs and platform services, optimize compute/storage, implement CI/CD and GitOps, and support recommendation/moderation systems. Mentor engineers, lead cross-team initiatives, run A/B testing and observability, and research emerging ML infrastructure technologies.

Build and expand in-database ML model catalog: implement new model types, ensure framework parity with scikit-learn/Spark ML, optimize training and inference at petabyte scale, extend SQL-level linear algebra, and collaborate across teams to deliver correct, performant ML features and documentation.

Build and maintain ML training and inference infrastructure, model APIs, and design pipelines. Partner with ML and protein design scientists to convert research into reproducible tooling and production services. Optimize compute and storage, implement CI/CD, monitoring, and follow engineering best practices to scale and accelerate protein design workflows.

Lead ML efforts for behavior and planning: design, train, and deploy state-of-the-art models (prediction, decision-making, RL/imitation, generative, transformers), own full model lifecycle from data to onboard inference, generalize behavior across geographies and platforms, collaborate cross-functionally, and mentor engineers.

Lead MLOps efforts to productionize machine learning models: build containerized deployments, automate CI/CD pipelines, manage Azure-based ML lifecycle, monitor model performance and data drift, collaborate with data engineering on pipelines, and mentor junior MLOps engineers while driving engineering best practices.

Design, build, and own ML evaluation systems and production-grade NLP/LLM solutions. Translate research into scalable agentic AI systems, collaborate with Research/Product/Platform, ensure quality, safety, and performance, and mentor engineers.

Lead hands-on research and engineering across agentic ML: train and fine-tune models, design evaluation and observability, build production improvement loops and tooling, prototype agent architectures, partner cross-functionally to productionize research, and set technical direction while mentoring senior engineers and scientists.

Design, build, and productionize ML systems focused on information extraction, retrieval, ranking, and recommendation. Own full ML lifecycle from feature engineering to deployment, observability, and evaluation. Collaborate with data, infra, and product teams to scale serving for streaming and batch inference and improve models using dataset engineering and graph/embedding techniques.

Lead design and deployment of production-grade agentic AI systems for healthcare. Architect scalable multi-agent orchestration, data curation pipelines, evaluation frameworks, and translate ML research to secure, low-latency clinical applications while mentoring engineers and collaborating with clinical and product teams.

Design and implement system-level debugging, validation, and observability platforms. Build automated anomaly collection/analysis, visualization and root-cause tools, failure classification and monitoring frameworks. Extend compilers, runtimes and programming interfaces for profiling and instrumentation, improve bring-up and low-level debug workflows, lead cross-functional initiatives, support incident response, and establish debuggability and reliability best practices.

Bring up and validate next-generation AI hardware systems; debug complex hardware-software integration issues using logs, telemetry and diagnostics; build automation, testing, and tooling to improve validation, observability, and debugging workflows; collaborate with hardware teams to reproduce, triage, and resolve failures as systems move toward production.

Drive end-to-end ML model inference performance: build kernel- and system-level performance models, optimize kernel microcode and compiler algorithms, debug runtime performance on system and cluster, and develop tooling to visualize and analyze performance data from the Wafer Scale Engine and compute cluster.

Design and build reinforcement-learning training environments and diverse tasks to evaluate and improve LLM agents; iterate rapidly on task designs from customer feedback, deliver high-quality outputs with minimal supervision, and maintain PST overlap for collaboration.

Develop and maintain high-performance C++ machine control and data analysis software for advanced mask inspection systems. Integrate AI/ML capabilities, collaborate with multidisciplinary engineering teams, optimize and scale existing code, and support integration/testing and customer escalations. Work with APIs, containers, and observability tools to enable reliable production deployment.

Design, build, and deploy production ML systems that combine structured clinical/EHR data with computer vision outputs. Own model calibration, interpretability, monitoring, drift detection, retraining workflows, and regulatory documentation. Partner cross-functionally, mentor engineers, and translate clinical questions into reliable, production-grade ML solutions.