Top Machine Learning Engineer Jobs

Designs, develops, and deploys end-to-end AI/ML solutions for financial services, including generative AI applications, LLMs, RAG systems, data pipelines, and production machine learning models. Builds AWS-based infrastructure and ETL workflows, performs model validation and risk assessments, establishes benchmarking and monitoring frameworks, and ensures compliance with banking regulations, privacy requirements, and ethical standards. Collaborates with technical and business stakeholders to identify AI opportunities and communicate complex concepts.

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.

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 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.

The Machine Learning Engineer will propose technical initiatives, lead design discussions, translate challenges into plans, and uphold engineering standards.

The Senior AI/ML Engineer will build scalable AI systems, work with LLMs, optimize models, and deploy solutions using AWS while ensuring ethical standards.

Lead technical direction for autonomous mineral refining by designing, validating, and deploying RL-based control models. Own autonomy roadmap, set simulator and modeling standards, solve sim-to-real and non-stationarity problems, partner with domain experts and leadership on techno-economic decisions, and mentor engineers while delivering production ML that runs on live plant equipment.

Develop and train reinforcement-learning control models in realistic simulators, refine training environments and reward logic, analyze and improve model performance, close the sim-to-real gap with plant data, write well-tested production code, and collaborate with process and chemistry experts to deploy autonomous controllers optimizing recovery, reagent use, energy, and uptime.

Design, build, and operationalize production-grade AI/Machine Learning solutions and MLOps services. Deploy models and API services to AWS and GCP using CI/CD and IaC, implement workflow automation, monitor post-production performance, research generative/ML techniques, and mentor junior engineers while collaborating with cross-functional data, cloud, and architecture teams.

As a Staff Machine Learning Engineer, you will develop, productionize, and deploy ML models for clinical use, ensuring real-world effectiveness through hands-on coding and infrastructure building.

Design, build, and scale core infrastructure for machine learning models, manage production systems, and implement best practices in MLOps.

Develop and fine-tune vision-centric large language models and visual encoders, build evaluation benchmarks, collect and prepare visual training data (including for RLHF), prototype architectures for production VLMs, publish research, and mentor junior researchers within a small applied ML research team.

The role involves leading the technical design and architecture of AI/ML platforms, managing cross-functional collaborations, and mentoring engineers while ensuring high performance and reliability of the systems.

Develop AI/ML models for 3D simulation and geology prediction, refine data pipelines, and integrate deep learning tech with product backend.

Lead the development of deep learning sensing technology by designing and deploying computer vision models for real-time inference, optimizing for edge deployment, and establishing MLOps pipelines.

Develop and deploy end-to-end machine learning solutions, collaborate with cross-functional teams, and implement MLOps frameworks while monitoring model performance in production.

As a ML Engineer at Wispr, you will design features for a voice interface, optimize ML inference, and scale personalization of speech models.

The Full Stack Developer will implement OpenAI and GPT-3 tools to develop processes for creating content and editing media for podcasts.

You will create large datasets and training recipes, develop methods for data mining, create scalable infrastructure solutions, and collaborate with ML infrastructure teams.

The Staff Machine Learning Engineer will develop and deploy advanced AI models and systems, enhance GenAI capabilities, and collaborate with cross-functional teams.

Develop C++ machine-control software and data analytics for advanced semiconductor mask inspection systems. Design, optimize, and maintain reliable, scalable code in multi-threaded and multi-process environments. Collaborate with electrical, mechanical, optical, algorithm, and software engineers to implement system requirements, support integration and testing, and resolve customer-site escalations. The role also involves debugging, Linux development, and potentially RPC, REST, containerization, Kubernetes, and modern UI frameworks.

The role involves building and optimizing compression technology, designing neural models, and enhancing training systems for efficient and effective codec production.

Design and develop scalable ML systems for healthcare, collaborating with teams to enhance products and maintain efficient infrastructures.

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.

The Senior Machine Learning Engineer will design and deploy AI systems, optimize production models, and ensure efficient performance in resource-constrained environments.