Top Machine Learning Engineer Jobs

Design, build, and operate high-performance inference platforms for LLMs, vision, and recommendation models. Optimize batching, GPU utilization, caching, autoscaling, routing, and observability. Integrate with API gateways and identity systems, run canary releases and incident response, and document operational procedures. Collaborate with ML and product teams to deploy and scale production AI services securely and cost-efficiently.

Design and ship applied ML solutions across NLP, vision, recommendations and structured data. Translate business problems to ML formulations, run rigorous experiments, build production training and inference pipelines, optimize for accuracy and latency, ensure observability and safety, and mentor engineers while influencing the AI roadmap.

Build and operate ML systems that take multimodal and video models from research prototypes to production: data ingestion, training/evaluation, serving, retrieval/vector search, orchestration for agentic reasoning, and ensure security, privacy, and reliability at scale while mentoring engineers.

Design, build, and operate high-performance ML inference platforms for LLMs, vision, and recommendation models. Optimize latency, throughput, GPU utilization, autoscaling, caching and routing. Integrate with API gateways, identity, and observability. Implement deployment workflows, canaries, incident response, security controls, and documentation. Collaborate with ML and product teams to support model rollouts and translate research into production features.

The role involves architecting AI solutions, optimizing ML systems, and deploying advanced retrieval frameworks. Engineers will lead ML lifecycle from research to production, collaborating across teams to build AI-first products.

Lead development and productionization of large-scale ranking and personalization ML systems. Build data pipelines, features, and models (batch and real-time), collaborate cross-functionally, run experiments, and improve infrastructure for search, recommendation, and marketplace optimization.

The role involves conducting R&D in machine learning, particularly in model training and architecture development. Candidates should have a background in deep learning and experience in hands-on experiments with foundational models.

The Senior Machine Learning Engineer will build and own production ML systems, manage end-to-end workflow, debug issues, and mentor others.

The job involves developing and optimizing machine learning models, overseeing model risk, and collaborating with teams on AI-related projects.

Lead the development of RL training frameworks and data processing pipelines for AV models, ensuring quality and scaling training workflows.

The Software Engineer will develop infrastructure for ML models, optimizing the economic engine for ride-hailing services and managing backend platforms for real-time decision-making in pricing and vehicle matching.

Design, build, and operate production AI platform capabilities and agentic workflows. Collaborate on architecture, write production-quality backend code, mentor developers, troubleshoot production issues, and drive adoption of reusable AI frameworks, MLOps practices, and cloud-native intelligent services across a multi-tenant SaaS dental platform.

Design, build, test, deploy, and maintain production AI capabilities (agents, workflows, models, MCP servers, APIs) across a SaaS dental platform. Train and operate ML models, implement RAG and vector DB solutions, use AI-assisted tools, collaborate in design and code reviews, and support production reliability and monitoring.

Lead hands-on AI/ML research and engineering to build AI-powered developer tools and platforms. Define technical strategy for LLMs, fine-tuning, and agents; conduct technical studies; collaborate with internal teams; design sustainable enterprise architectures; mentor and grow ML engineering teams; and communicate complex technical concepts to diverse stakeholders.

As a Software Development Engineer (AI & ML), you'll architect and implement scalable ML models, optimize data pipelines, and collaborate with teams to deliver financial tech solutions.

Design, optimize, and operate production LLM inference and training pipelines. Improve latency, throughput, and GPU utilization using techniques like batching, quantization, FlashAttention, and kernel-level profiling. Deploy and autoscale multiple models on shared GKE GPU clusters, consult with clients on performance and cost requirements, and carry prototypes to robust, scalable production services.

Design, develop, and deploy production-grade machine learning models and pipelines. Preprocess and analyze large datasets, collaborate with cross-functional teams, monitor model performance, and implement scalable ML solutions to improve customer experiences.

Lead operational excellence for enterprise AI/ML platforms: ensure reliability, scalability, observability, governance, and deployment readiness. Build automation, CI/CD, IaC, monitoring, and model lifecycle processes. Serve as senior escalation for incidents, drive root cause analysis, mentor engineers, and partner across Data, Infrastructure, Security, and Product teams to enable production AI workloads and generative AI adoption.

Lead research and production deployment of ML and computer vision systems for 3D capture and reconstruction. Improve camera pose estimation, mesh/surface reconstruction, texturing, semantic understanding, and real-time capture guidance. Apply deep learning, multi-view geometry, and neural rendering techniques; collaborate across hardware, software, and product teams; and manage the full ML lifecycle from data collection to production monitoring. Write high-quality Python and C++ code.

Build and maintain ETL/ELT pipelines in a lakehouse/cloud environment, ensure data quality and lineage, and apply classical statistics and ML (anomaly detection, regression, time-series) to support operational decision-making and stakeholder reporting.

Lead design, build, and productionalize generative AI applications and platform capabilities. Automate ML pipelines, optimize models (LoRA/QLoRA/PEFT), implement guardrails and monitoring, support RAG and vector DB tuning, mentor engineers, and collaborate with product and data teams to deploy safe, reliable GenAI features across web and mobile.

Design, develop, and deploy end-to-end AI/ML applications (RAG, chatbots, agentic workflows). Build scalable data pipelines, apply graph analytics, entity resolution, and NLP for risk scoring, anomaly detection, and investigative prioritization. Develop models, REST APIs, and deploy/manage solutions in AWS to support federal mission use cases.

Design, build, and deploy large-scale distributed AI/ML applications (IVR, voice, chat) including multi-agent systems and LLMs. Collaborate with product and data science, ensure reliability, scalability, monitoring, and bring research-driven innovations into production using cloud, containers, and IaC practices.

As a Senior Machine Learning Engineer, you'll design and develop infrastructure and models for AI-powered applications while collaborating with teams to enhance product functionality.

As a Senior Machine Learning Engineer, you'll design and deploy ML models, build cloud-native solutions, and mentor junior engineers within healthcare technology.