Top Machine Learning Engineer Jobs

Develop and maintain C++ machine control and data analysis software for advanced mask inspection systems. Collaborate with multidisciplinary teams to define system requirements, optimize performance, support integration and testing, and resolve customer escalations. Work in Linux environments and leverage RPC/REST APIs, containerization, and modern UI frameworks as needed.

Develop and optimize multi-modal (Lidar, Camera, Radar) 3D occupancy, segmentation, and flow models. Architect temporal and vision-first fusion approaches, ensure temporal consistency, and optimize networks for real-time on-vehicle inference. Collaborate with tracking, prediction, and planning teams to deliver precise geometric and motion outputs for autonomous navigation.

Develop algorithms in machine learning and statistics to address complex challenges. Design data pipelines and deploy solutions using cloud infrastructure.

The Senior AI/ML Engineer will develop and enhance AI/ML models using Python, specializing in OPIR domain expertise, with a focus on object identification and classification.

Design and implement production ML algorithms for real-time streaming telemetry at massive scale, optimized for resource-constrained environments. Build end-to-end ML pipelines, collaborate with Data Science to translate research into production, and improve anomaly detection, health scoring, and predictive analytics for GPU and network infrastructure.

The ML Engineer will develop and deploy machine learning models, collaborate with teams, analyze datasets, and improve model performance.

Design, implement, and operate scalable ML runtime microservices and DevOps tooling. Build CI/CD and infrastructure-as-code, deploy containers with Kubernetes/ArgoCD, collaborate with ML teams, own features end-to-end, provide on-call support, and drive platform reliability, monitoring, and developer self-service.

Design, prototype, and ship LLM- and agent-powered AI features for observability. Rapidly iterate with real users, integrate AI workflows with tooling and alerting, collaborate cross-functionally, and ensure scalable, production-ready solutions in cloud-native environments.

Build and operate large-scale ML training infrastructure: distributed GPU training, multi-cluster scheduling, data pipelines (batch and streaming), ML workflows, observability, reliability, and on-call incident response to keep autonomy model training running efficiently.

Build and operate large-scale ML training infrastructure: distributed GPU training, multi-cluster scheduling, data pipelines (batch & streaming), ML workflows, observability, alerting, and on-call/incident response to ensure reliable, cost-effective model training and releases.

Design, build, and maintain ML infrastructure for model lifecycle: training, optimization, validation, deployment, inference, and observability. Own ML inference and compiler platforms, develop scalable ML pipelines, continuous testing/monitoring, and support deployment to diverse hardware for autonomous vehicles.

Build and maintain core ML infrastructure: design and implement ML training, optimization, validation, and deployment pipelines; operate an in-house ML inference and compiler platform; implement testing, monitoring, and observability for model lifecycles; collaborate with cross-functional teams to deploy optimized models to vehicle hardware.

Design, build, and deploy machine learning and generative AI models for real-time identity threat detection. Develop data ingestion pipelines and distributed platforms, improve test coverage and monitoring (LiveSite), troubleshoot production incidents, and collaborate with data scientists to operationalize ML-driven security detections.

Design, build, and own production-grade LLM and agentic AI systems, including evaluation pipelines, failure diagnosis, retrieval and orchestration, data-driven improvement loops, deployment, and observability while collaborating with clinicians and product teams.

Lead design, development, deployment, and maintenance of large-scale ML systems and pipelines. Provide senior technical consulting, resolve complex ML and governance issues, plan end-to-end model integrations, and document implementations for performance optimization.

Design, build, and operate petabyte-scale data systems and pipelines to supply high-throughput, versioned, and lineage-tracked datasets to ML training and evaluation jobs. Ensure data quality, reproducibility, and efficient delivery for accelerator-based training while collaborating across teams and following strong software engineering practices.

Design, build, and operate large-scale, low-latency online model inference backend services. Drive inference platform direction, ensure production reliability/observability, manage GCP cloud infrastructure and Kubernetes, and collaborate with ML engineers to scale serving performance.

Lead design and implementation of agentic AI systems in the Unity engine, developing scalable RL and planning frameworks, integrating ML models under real-time constraints, establishing evaluation and safety practices, and mentoring engineers to deliver reusable, performant agent behavior for games and simulations.

Lead design and shipping of large-scale optimization algorithms for ad campaigns, balancing multi-objective goals, exploration-exploitation strategies, and long-term impact measurement. Build metrics frameworks, LLM-based agentic automation for diagnostics and recommendations, and provide cross-functional technical leadership from model design to real-time production auctions.

Design and scale real-time bidding systems and bid forecasting; develop ML, reinforcement learning, and control-based bidding and pacing algorithms; analyze marketplace dynamics; run offline evaluations and online A/B experiments; collaborate with product, infrastructure, and engineering teams to deliver measurable advertiser ROI.

Build and operate scalable data pipelines and feature datasets for ads ML. Own end-to-end offline flows from event ingestion to feature-ready datasets, develop batch and streaming systems, integrate feature store with online serving, improve observability and testing, and lead architectural decisions to ensure performance, correctness, and reliability at scale.

Lead research and engineering for ads recommendation and user behavior models. Rapidly validate models using large-scale data and compute, design metrics and analyses, and collaborate with engineering and product teams to productionalize solutions that deliver measurable advertising business impact.

Design, build, and operate large-scale online ML inference infrastructure to serve production models with low latency and high reliability. Optimize inference performance, enable distributed training workflows, integrate ML pipelines with orchestration systems, improve observability, and support safe model rollout, canary testing, and automated rollback. Lead architectural improvements and collaborate with ML engineers and product teams to scale, monitor, and streamline model deployment and iteration.

Design, optimize, and scale training and inference workloads for personalization and continual learning. Productionize research prototypes, improve latency/reliability/efficiency, optimize per-user serving and memory retrieval, and work on distributed training across GPUs and nodes. Collaborate with researchers, platform engineers, and customers; establish engineering practices, testing, on-call, and security posture.

Design, build, and operate ML infrastructure for training, evaluation, deployment, and inference. Improve reliability, scalability, latency, throughput, and cost. Create pipelines, observability, benchmarking, and tooling to enable fast experimentation and productionization of models while diagnosing regressions and bottlenecks.