Top Machine Learning Engineer Jobs

Lead ML Performance Optimization for autonomous driving, collaborating across teams, implementing optimization techniques, and mentoring engineers for career growth.

Design and develop auto-labeling algorithms for autonomous driving, collaborating with ML engineers to enhance data quality and efficiency in model training.

Build and scale AI/ML-enabled features across frontend, backend, and data pipelines. Productionize ML models, deploy MLOps workflows, integrate inference APIs, optimize data models, ensure reliability, and collaborate with cross-functional teams for mission-critical analytics.

As a Senior Machine Learning Engineer at ZoomInfo, you will enhance core datasets through ML/NLP solutions, manage entity resolution tasks, and drive cross-functional impact by mentoring teams and collaborating on ML KPIs.

Develop spatio-temporal machine learning models for automated driving, collaborate with experts on algorithm design, and ensure production-level implementation of perception systems.

Develop and prototype AI/ML-driven automation for ASIC/SoC design and implementation, collaborating with academia and cross-functional teams to apply GenAI, RL, GNNs and CNNs to VLSI CAD flows, pilot solutions on real designs, and improve power, performance, area, and quality.

Build and maintain ML infrastructure and data systems for medical imaging: distributed training, RL training stack, high-throughput data pipelines, centralized storage, and production serving to accelerate research-to-production iteration.

Build and maintain scalable ML and generative AI platforms and backend infrastructure; deploy and manage production applications; create services exposing ML/AI products; implement RAG solutions and vector databases; integrate LLM orchestration and foundation model APIs; collaborate cross-functionally and mentor teammates.

Lead development of large-scale ranking, personalization, and recommendation systems for Roku's entertainment assistant. Design and deploy ML models (LLMs, transformers, RL, bandits), build feature platforms and data pipelines, run online A/B experiments, and mentor engineers while balancing engagement, retention, relevance, and monetization objectives.

Lead development, training, optimization, and edge deployment of perception models for maritime autonomous vehicles. Curate fleet data, run measurable ML experiments, build data pipelines, establish evaluation frameworks, and integrate models with vehicle software to validate real-time performance on embedded hardware.

Design, build, and maintain batch and streaming data pipelines on GCP (BigQuery, dbt, Airflow, Pub/Sub). Productionize ML workflows (training, inference, CI/CD) using Cloud Run, GKE, and Vertex AI. Support real-time inference, RAG/LLM assistants, graph-based recommendations, and model monitoring. Collaborate with cross-functional teams to deliver scalable, reliable data products and observability for downstream ML and analytics.

Design, build, and maintain end-to-end ML pipelines and production deployments using AWS SageMaker. Develop feature engineering and data pipelines, automate CI/CD model deployments, implement monitoring and drift detection, manage versioning and experiment tracking, optimize models for performance and cost, and support production operations and incident response.

Lead design, training, fine-tuning, and deployment of ML and generative models (LLMs, diffusion). Build scalable MLOps pipelines, vectorization and data ingestion, integrate models into microservices for low-latency inference, implement evaluation/alignment/bias-mitigation, optimize models (quantization, distillation), and mentor engineers while influencing the AI product roadmap.

Design, build, and ship agentic AI systems and a Perplexity-style conversational search UI for Humana. Implement retrieval, planning, tool-use, grounding, and evaluation pipelines; integrate heterogeneous internal data sources; deploy and operate solutions on GCP; ensure observability, guardrails, and continual quality improvements while collaborating on-site with a cross-functional pod.

Design, build, and deploy high-throughput ML and LLM-powered personalization and agentic systems at consumer scale. Process petabyte-scale data, optimize real-time inference, implement resilient fallback paths, and balance latency, cost, and model quality while collaborating across engineering and product teams.

Build and operate cloud-native, large-scale data pipelines and scalable services for the Mist platform. Develop AI-driven routing intelligence to detect, troubleshoot, predict, and mitigate network issues in multi-tenant data centers, collaborating across teams and leveraging AI-assisted development tools.

Build and maintain scalable backend services, APIs, and data pipelines for computer vision and LLM workflows. Integrate model serving, ensure reliability, performance, and observability, collaborate with ML and Solutions Engineering, and support production deployments and occasional on-site testing.

Own end-to-end CTR/CVR prediction models in a real-time bidding system (sub-100ms): modeling, feature pipelines, offline and online evaluation, A/B testing, experiment debugging, and post-launch ops. Hunt for new predictive signals, build tooling for experiment debugging and signal discovery, collaborate with data and pipeline teams, and set technical direction for NEXT's ML stack.

Design, develop, and deploy AI/ML solutions for wired and wireless signaling data. Implement and benchmark neural networks, optimize algorithm performance, support Agile software development, collaborate cross-functionally, and mentor junior staff. Work requires eligibility for TS/SCI with polygraph and a STEM bachelor's degree.

Lead architecture and development of distributed, large-scale ML systems for Merlin Bird ID and related platforms. Build training pipelines, production deployments, APIs, and front-end integrations. Mentor engineers, set technical direction, ensure scalability, performance, reliability, and collaborate with engineers, scientists, and domain experts to deliver global conservation technology.

Design, build, and maintain MLOps pipelines and infrastructure for Traditional, Generative, and Agentic AI. Implement CI/CD, containerized deployments (OpenShift/Docker/Kubernetes), cloud integration (AWS), automation (Bash/Python), logging/observability, security controls, and collaborate with data scientists and DevOps to deploy and optimize production AI systems.

Lead design, build, and modernization of recommendation systems integrating backend engineering, data pipelines, and LLMs. Develop, test, deploy, and maintain scalable microservices and MLOps solutions, mentor engineers, collaborate with product teams, and drive system design, monitoring, and retraining pipelines for production ML.

The Senior Machine Learning Engineer will design, build, and deploy scalable ML systems, collaborating closely with teams to optimize AI processes and mentoring juniors.

Lead design and implementation of cloud-native, microservices backend systems that power AI-enabled features (LLM assistants, RAG pipelines, recommendations). Build scalable REST and event-driven APIs, establish MLOps practices, ensure reliability/security/observability, mentor engineers, and collaborate with product, scientific, and infrastructure teams to productionize AI services in regulated scientific environments.

Develop scalable web applications using React and Next.js, implement backend solutions in Python, maintain DevOps practices, and collaborate with AI/ML teams to deploy applications.