Top Machine Learning Engineer Jobs

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.

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 train foundation vision-language models that read entire CT studies end-to-end. Build custom PyTorch architectures and distributed training pipelines, run large-scale experiments, evaluate models for clinical safety, and move models through FDA submission to hospital deployment.

Develop end-to-end foundation models that read entire CT studies and generate radiology-style reports. Build vision-language and 3D models, run large-scale distributed training and rigorous experiments, and deliver models that progress through FDA clearance to hospital deployment. Collaborate with clinicians and engineers to ensure safety, reproducibility, and clinical impact.

Lead architecture and delivery of frontier AI solutions: define end-to-end technical designs, establish reusable AI engineering patterns, guide platform integration and production readiness, advise cross-functional stakeholders, and ensure secure, scalable, observable AI products that are commercially viable.

Build and maintain backend services and infrastructure for ML and generative AI features. Develop scalable, resilient, high-performance Python services (async frameworks, streaming), deploy to Kubernetes, integrate RAG and LLM orchestration, instrument LLM observability, and collaborate cross-functionally to deliver production-ready AI products.

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 Senior ML Systems Engineer will build and maintain the training framework for large-scale language models, focusing on distributed training and performance optimization.

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

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

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

Build systems for Mercor's hiring engine, including pipelines for indexing, sourcing, scoring, and conversion. Work with ML and backend technologies.

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.

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.

The role involves optimizing machine learning models for autonomous driving by implementing performance techniques and collaborating with cross-functional teams on architectural decisions.

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.

Senior individual-contributor building production agentic AI and RAG systems end-to-end: document ingestion/OCR, chunking, embeddings/vector indexes, retrieval orchestration, prompt engineering, evaluation harnesses, FastAPI services, database schemas, governance controls, and mentoring mid-level engineers.

Lead architecture and delivery of production-grade agentic AI and RAG systems. Own multi-agent orchestration, embedding/vectorization pipelines, retrieval, evaluation frameworks, and team leadership for engineers building document-intelligence and AI-assisted developer tooling. Responsible for production delivery, observability, CI/CD, and cross-team integration in an enterprise finance environment.

Nagarro is looking for an Associate Staff Engineer specializing in Machine Learning to tackle client challenges and enhance team collaboration, while pushing solutions further.

Own end-to-end clinical prediction models: design, train, evaluate, and ship models using physiological time series and EHR data. Define labels with clinical teams, build evaluation/backtesting and regression infrastructure, assess robustness and subgroup performance, support production deployment and monitoring, and contribute to validation and regulatory evidence.

Design, build, and deploy large-scale ML systems for ad ranking, bid optimization, and recommendations. Build and operate production ML pipelines (feature engineering, distributed training, deployment, retraining, monitoring) that process billions of events and serve low-latency predictions. Optimize training and inference for compute and cost efficiency. Partner with Data Science, Data Engineering, and product teams to ship models, run online experiments, and measure CTR/CVR/ROI impact. Incorporate applied ML research and techniques into advertising and audience modeling.

Lead technical vision and roadmap for a company-wide ML platform: define reference architectures, MLOps best practices, security, observability, and integrations; mentor engineers and drive cross-team platform initiatives.

The job involves developing machine learning algorithms, designing data pipelines, utilizing cloud infrastructure for ML solutions, and ensuring model governance practices.

Design, own, and scale training and inference infrastructure for a robotic fleet. Build data pipelines, optimize distributed training and GPU utilization, speed experiment launch and reproducibility, and contribute to core training code.