Top Machine Learning Engineer Jobs

Design and develop data solutions, optimize ETL/ELT pipelines, integrate ML models, collaborate with engineers, and operationalize analytics for semiconductor R&D and operations.

Develop machine learning models and pipelines for fish farms, interacting with databases, conducting data analytics, and building statistical models.

As a Machine Learning Engineer, develop ML models, data pipelines, conduct analytics, and work on image and video inference for fish health and behavior.

Develop systems and APIs for LLM inference and fine-tuning. Design production systems, collaborate with teams, and improve system efficiency.

The role involves building and deploying machine learning solutions, collaborating with stakeholders, and ensuring production-ready systems, with responsibilities across the ML lifecycle.

Assist in designing, developing, and deploying machine learning models for creating high-quality 3D assets while collaborating with cross-functional teams.

As a Machine Learning Engineer, you will apply ML techniques for perception tasks, develop model training methods, and evaluate performance in autonomous driving applications.

As a Staff Machine Learning Engineer - AI Tech Lead, you will lead the design of AI systems for security operations, focusing on scalable architectures, AI safety, and mentoring. Responsibilities include overseeing technical evaluations, production workflows, LLM fine-tuning, and collaboration with teams to enhance AI capabilities.

As a Sr. AI/ML Engineer III, you will design and implement reinforcement learning algorithms, integrate them into simulation environments, and evaluate their performance. Collaborating with engineering teams, you will ensure the maintainability and transparency of AI components while communicating results and recommendations clearly.

Lead technical direction and execution of Lime's CX automation platform, building scalable backend services, decision engines, and human-in-the-loop workflows to automate rider issue resolution while enforcing fraud and trust guardrails and improving operational metrics.

Build and maintain enterprise-scale ML/LLM infrastructure on Azure: create automated, secure CI/CD pipelines, provision resources with Terraform, enable real-time inference and RAG pipelines, and collaborate with data science and product teams to productionize generative AI solutions.

Lead development and scaling of panoptic (instance + semantic) and 4D occupancy segmentation models. Collaborate on efficient architectures, labeling workflows, diagnostics, metrics, and deployment—using large pre-trained models and integrating with detection/tracking.

Lead end-to-end productionization of ML models: build pipelines, deploy real-time and batch inference, manage model registry/monitoring, integrate with AWS and Snowflake, and enable reproducible, scalable ML workflows.

Design and build foundation-scale datasets and training recipes; develop human and machine labeling methods; create continuous data intake and automated data collection/model update pipelines; evaluate real-world model performance and detect regressions; maintain ground-truth-free performance metrics.

Develop and maintain machine learning models and pipelines as part of a cross-functional data team. Build/operate ML platforms, use data processing frameworks, apply software engineering practices, obtain Security+ and support classified (Top Secret/SCI) environments while working onsite in Oahu with travel up to 30%.

Develop, scale, and monitor ML and perception systems for autonomous vehicles. Analyze real-world ML behavior, root-cause issues, create automated detection and debugging tools, and maintain safety-critical metrics.

Apply AI/ML and data-driven techniques to accelerate NAND flash controller design verification. Build models and tooling for failure triage, anomaly detection, and root-cause analysis, integrate ML solutions with verification pipelines, and collaborate with design, DV, firmware, and infrastructure teams to improve debug efficiency and coverage closure.

Design, build, and maintain scalable AI/ML architectures; collaborate with data scientists, engineers, product, and compliance to integrate models; evaluate tools and processes; monitor performance and ensure industry-standard compliance and ethical practices.

Design and build core ML systems for AI agents, managing the lifecycle from problem framing to deployment, ensuring solutions are scalable and enterprise-ready.

Develop intelligent AI solutions for enterprise operations, design robust ML architectures, and mentor junior engineers. Collaborate with teams to deliver results.

Design, deploy, and maintain production-grade agentic and ML systems. Build evaluation, monitoring, and experimentation tooling; automate ML lifecycle and retraining; QA agent behaviors; collaborate cross-functionally and communicate findings to stakeholders.

The Machine Learning Engineer will design and implement advanced ML models for energy systems, collaborating with R&D teams to tackle challenges in battery technology.

The role involves designing and implementing ML models for battery and energy systems, collaborating with R&D teams, and conducting rigorous experimentation.

As the first Developer Advocate at Encord, you will educate the community, create technical content, and engage in ML/CV networks and events.

As a Machine Learning Engineer, you will develop deep generative models, create efficient data pipelines, and evaluate complex biological models in a biotech startup.