Top Machine Learning Engineer Jobs

The Performance Engineer - Inference will optimize model inference speed and throughput, debug low-level kernel performance, and develop tools to visualize performance data.

Design and implement system-level debugging, validation, and observability platforms. Build automated anomaly detection, visualization and analysis tools, and frameworks for failure classification and regression detection. Extend compilers, runtimes, and instrumentation for advanced profiling. Improve bring-up and low-level debug workflows, partner cross-functionally across hardware, firmware, compiler and runtime teams, lead high-impact initiatives, and support incident response and long-term corrective actions.

Operate and monitor ML/AI models and agentic systems in production. Build AI observability, logging, tracing, and evaluation pipelines. Monitor LLM outputs, detect drift and model degradation, and maintain data/feature pipelines. Develop CI/CD, model versioning, experiment tracking, and automate alerts and incident remediation while collaborating with data scientists and platform teams.

Design, develop, fine-tune, and deploy generative AI models using deep learning and transformer architectures. Collaborate cross-functionally, troubleshoot model issues, optimize performance, document work, and communicate technical concepts to non-technical stakeholders.

Design, implement, and own end-to-end scalable ML systems and MLOps lifecycle (deployment, monitoring, metrics). Optimize models and system performance, work with Java/Python and TF/PyTorch, use CI/CD, Terraform, Docker/Kubernetes, lead inter-team communication, document work, and meet deadlines.

The Data/ML Infrastructure Engineer builds and operates data infrastructures for processing drone and orbital sensing data, ensuring reliability, observability, and performance while collaborating with research and product teams.

Design, build, and deploy production ML models and scalable pipelines to improve recommendations, ranking, and personalization. Own end-to-end workflows from data exploration and feature engineering to training, evaluation, experimentation (A/B tests), and production deployment while applying responsible AI practices.

Design, build, and operate CV/ML infrastructure: manage 130+ GPU bare-metal Kubernetes cluster with AWS burst, own CI/CD for ML, enable model/versioning, experiment tracking, testing, performance analysis, and optimize models for edge deployment (Jetson).

The Senior Machine Learning Engineer will design, implement, and scale advanced foundation models, collaborate with researchers, and build AI systems. Requires hands-on experience with distributed training pipelines and proficiency in AI frameworks.

Lead development of machine learning, computer vision, sensor fusion, and registration algorithms for a robotic lung biopsy platform. Prototype, evaluate, and productionize image-analysis solutions in Python/C++, collaborate cross-functionally, perform failure-mode assessment, and maintain state-of-the-art technical knowledge.

Design, train, and evaluate computer-vision perception models (anatomy, instruments, actions) for surgical video; develop temporal/video models and bench-mark SOTA; define perception I/O and move models from offline experiments to robust, real-time OR performance; establish continuous improvement loops, annotation pipelines, and partner cross-functionally to enable prototype-to-product deployment.

Develop real-time intraoperative ML and computer vision software for robotic-assisted minimally invasive surgery. Design, prototype, implement, test, and deploy algorithms and user-facing components, support system integration, produce documentation and patents, and iterate from clinical feedback to deliver production-ready code.

The Software Engineer III designs and develops complex software solutions, provides application support, and improves operations through systems analysis and recommendations.

Design and implement production ML features, close behavior gaps with common frameworks, investigate divergences, improve performance and stability, and document designs and tests for geospatial and large-scale ML workflows.

Design and deploy autonomous, conversational AI agents for patient engagement: build multi-agent systems, integrate with EHRs/APIs, fine-tune models, implement safety guardrails and evaluation frameworks, and monitor production performance.

Design, extend, and maintain distributed training infrastructure: modify frameworks (DeepSpeed, FSDP, etc.), implement distributed optimizers, build multi-node launch/config systems, implement experiment tracking and monitoring, optimize reliability and performance, and write production-quality ML infra code in PyTorch or JAX.

Customer-facing technical role supporting AI/ML initiatives across US Major Accounts. Architect and prototype AI/ML solutions, build MVPs and PoCs, support regional SE teams, deliver technical presentations and workshops, and upskill teams. Travel ~30-40% to support customer engagements.

Design, implement, and evaluate perception algorithms and 4D world representations for autonomous vehicles. Train and deploy ML and computer vision models from data collection to production, test on real vehicles, and collaborate with data, behavior, and research teams to deliver production-ready autonomy software.

Optimize and deploy ML models to production embedded runtimes. Improve inference latency and efficiency across accelerators, prune/quantize models for memory‑constrained platforms, profile on target hardware, and collaborate with ML and software teams on architecture and integration.

Build and operate end-to-end ML infrastructure: distributed cloud GPU training, datasets, training frameworks, evaluation, deployment, and integrate pipelines into product workflows while collaborating with modeling teams.

Build and maintain production AI/ML models and pipelines for an NDR platform; develop scalable, secure backend services; optimize detection algorithms and reduce false positives; integrate ML features into product architecture; mentor engineers; participate in code reviews, technical strategy, and documentation; operate cloud-native, containerized services and manage infrastructure as code.

The role involves designing and deploying machine learning systems for content safety at Spotify, collaborating with various stakeholders, and providing technical leadership.

The role focuses on leading AI/ML architecture and deployment for scalable solutions in cloud security, shaping technical strategies, and managing the lifecycle of AI models.

Lead integration strategy and execution for Zoox's agentic platform. Collaborate with teams on tool integration, debug failures, and ensure resource readiness for production. Define integration artifacts and contribute to release processes.

Lead training, evaluation, and deployment of ML models for scene understanding and behavior prediction in autonomous vehicles.