Top Machine Learning Engineer Jobs

You will train, evaluate, and deploy ML models for scene understanding and behavior prediction in autonomous vehicles, while collaborating with engineers.

The role involves developing and deploying machine learning solutions, optimizing models, and collaborating with cross-functional teams. Responsibilities include cloud architecture, algorithm engineering, and infrastructure best practices.

The MLOps Engineer focuses on developing tools and processes for ML workflows, empowering engineers, and ensuring high-quality model deployment. Key responsibilities include managing distributed systems, improving observability, and advocating best practices across teams.

The Solutions Engineer will engage with clients, develop technical solutions in Robotics, and guide customer pilots, ensuring satisfaction and driving commercial growth.

Design and build scalable, fault-tolerant ML serving platforms for robotics. Optimize computer-vision and foundation-model pipelines for cloud GPUs, develop observability and monitoring, conduct architecture reviews, and lead end-to-end projects integrating research and production.

Build and maintain Lyft's ETA prediction system: analyze data, prototype ML and non-ML solutions, develop statistical/ML/optimization models, trade off accuracy versus production/runtime, write scalable production-quality code, participate in design/code reviews, on-call support, and incident triage.

Design, build, and maintain ML infrastructure and data pipelines; develop, deploy, and monitor underwriting and financial-service models in production; run A/B tests and experiments; collaborate with data scientists, engineers, and product to integrate models; mentor junior engineers and implement continuous improvement for model performance and scalability.

Design and productionize ML/DL algorithms and agentic LLM triage systems, run and maintain multiple production data pipelines (Databricks/AWS), monitor and fix pipeline issues, build monitoring tools and dashboards, and collaborate with data science, planning, safety, and QA stakeholders.

As a Senior AI/ML Platform Engineer, you will architect and scale the ML platform, ensuring efficient ML workflows and collaborating with various teams to drive AI adoption.

Responsible for developing machine learning capabilities, partnering with Data Scientists, and enhancing customer experiences through AI solutions.

Develop and train computer vision and multimodal models, design scalable AI frameworks, and improve systems through fine-tuning and reinforcement learning techniques while collaborating with multiple teams.

Design, build, and ship AI-powered observability features using LLMs and agentic workflows. Rapidly prototype, iterate with real users, integrate AI into alerting, runbooks, and developer workflows, and ensure solutions are scalable, maintainable, and production-ready while collaborating cross-functionally.

Design, develop, and maintain real-time deep learning pipelines and production ML microservices on Kubernetes. Curate datasets, establish benchmarks, improve observability and monitoring, and scale ML workflows. Collaborate with product, backend, and systems teams, contribute to R&D for aerospace/defense use cases, and document operational best practices.

Design and implement scalable offline and online ML infrastructure for Search & Discovery: build data logging, ETL, training pipelines, and real-time model serving for ranking and recommendations, collaborate with cross-functional teams, and mentor engineers.

Lead and contribute to applied AI/ML projects across the SDLC: mentor teams, design and implement LLM-based solutions (RAG, copilots), architect integrations, manage data persistence choices, CI/CD, and present solutions to clients while influencing technical direction.

Lead and mentor engineering teams in applied AI/ML projects. Define architectures, propose technologies, build reference implementations, contribute code and CI/CD pipelines, and work with clients on enterprise deployments (LLMs, MLOps, RAG, vector/graph databases). Shape engineering practices and influence business development.

Build and maintain production data pipelines and infrastructure to prepare multimodal conversational, voice, sensor, and telemetry data for ML training and evaluation. Implement dataset versioning, lineage, quality monitoring, and tooling to enable reproducible, discoverable datasets and cost/performance-optimized large-scale processing while enforcing data governance and privacy.

The Senior Machine Learning Engineer will automate healthcare compliance workflows using AI to process unstructured data, build multimodal AI pipelines, and develop extraction systems, ensuring high reliability and operational efficiency.

Develop and implement image processing and machine learning algorithms, collaborate with engineers, and demonstrate algorithm performance for semiconductor process development.

Design and build distributed ML infrastructures to optimize data processing, model training, and evaluation among teams.

Lead development of ML infrastructure and pipelines for real-time personalization and recommendations. Own end-to-end initiatives from design to production, drive scalable training and inference, ensure observability and CI/CD, mentor engineers, and collaborate with product and editorial stakeholders to deliver low-latency, personalized news experiences at scale.

The role involves integrating and productionizing ML models into infrastructure, optimizing pipelines for real-time inference, and enhancing GPU workloads for robotics applications.

The role involves supporting ML infrastructure, building diagnostic tools, managing deployments, and optimizing GPU utilization for ML projects.

The Senior Machine Learning Engineer will manage the full ML lifecycle, collaborating with teams to develop scalable systems that improve product decisions and empower monetization strategies.

Optimize the performance of ML models for training and inference. Focus on CUDA optimization and systems approach, ensuring low latency and high throughput in real-time. Debug and utilize modern tools and techniques for system performance improvements.