Top Machine Learning Engineer Jobs

As an ML Engineer at Luma AI, you will design high-performance model serving pipelines, manage GPU resources, and optimize CI/CD for large-scale AI systems.

Lead ML/AI analytics across STARZ telemetry and operational data to detect anomalies, forecast trends, and surface actionable signals. Build repeatable signals frameworks, apply anomaly detection/classification/clustering/time-series models, leverage generative AI/LLMs for summarization and RCA, define alerting and escalation, partner with engineering and product, and mentor junior analysts.

The role involves developing advanced machine learning algorithms, solving complex optimization problems using reinforcement learning techniques, and leading technical projects while optimizing solutions for operational efficiency.

As a Software Engineer focusing on ML Platform at Sola, you'll turn AI research into production features, build observability systems, and collaborate with the CTO, taking ownership of AI functionalities end-to-end.

As a Senior Machine Learning Platform Engineer, you will manage ML infrastructure, optimize pipelines, mentor junior engineers, and ensure observability and compliance in production environments.

As a Machine Learning Infrastructure Engineer, you'll design scalable infrastructure for AI research, manage deployments, and ensure reliable customer solutions, working directly with the CEO.

Lead the development of AI/ML deployment infrastructure, collaborate with engineers, develop pipelines, manage teams, and enhance data applications.

Design and build Machine Learning libraries, implement CI/CD pipelines, and support Ray-based compute environments, collaborating with ML experts.

As a Senior Machine Learning Engineer, you will architect a scalable ML platform, collaborate across teams, and ensure best practices for ML workflows and operations.

Design and deploy ML models and platforms for Axon products. Collaborate with scientists and PMs to build POCs, implement training techniques, enable secure privacy-preserving model improvements, and accelerate research-to-production pipelines.

Manage distributed machine learning workloads using Slurm and Kubernetes, ensuring cluster operations and mentoring engineers in best practices.

Build and evolve AI systems to enhance investment insights, integrating models and optimizing outputs for professional investors in a fast-paced environment.

The Senior AI/ML Software Developer will design, develop, and maintain AI applications, collaborating with teams and solving complex problems.

Develop robust AI/ML solutions for manufacturing, collaborating with scientists to build scalable infrastructure, optimizing ML systems, and ensuring code quality.

Design, build, and maintain secure on‑prem and cloud infrastructure and CI/CD pipelines for Kubernetes-based services. Automate deployments, manage production/staging/dev environments, monitor logs and system health, enforce security controls, and support accreditation (A&A) and operational readiness. Troubleshoot incidents, perform root cause analysis, maintain documentation, and collaborate with Agile teams to drive DevSecOps best practices.

Lead a small team building data and ML infrastructure for large-scale medical imaging ML. Architect and implement distributed compute platforms (training and inference), cloud-data systems, and production deployments. Mentor engineers, write high-performance Python code (including cross-language bindings), use frameworks like Ray and Kubernetes, apply infrastructure-as-code, and collaborate with researchers to enable scalable, secure ML workflows and monitoring.

Build and operate ML infrastructure for large-scale biological foundation models: dispatch distributed training across multi-cloud GPU clusters, deploy high-throughput storage, implement evaluation and experiment-tracking tooling, and integrate training with telemetry and checkpointing systems.

Develop efficient ML solutions for perception including sensor fusion, obstacle detection, scene understanding, and robust onboard systems.

As a Senior ML Engineer, you will develop ML pipelines, work with language models, and build agentic AI systems while implementing MLOps practices.

The Staff Machine Learning Engineer will develop and maintain MLOps tools, optimize model performance, and collaborate on ML system architecture with a focus on enhancing developer experience.

The role involves designing scalable distributed systems using Scala, enhancing feature stores for performance, and collaborating with ML engineers to resolve challenges.

The role involves conducting deep learning research, building training pipelines, enhancing frameworks, and collaborating with teams to improve trading strategies.

The Machine Learning Performance Engineer will build training and inference pipelines, enhance deep learning frameworks, improve performance, and collaborate with researchers and engineers to optimize trading systems.

Deploy, maintain, and improve production ML workflows and platforms for media measurement and customer analytics on AWS. Build SageMaker jobs and endpoints, orchestrate pipelines with Step Functions, Lambda, and Airflow, manage model artifacts and CI/CD, troubleshoot production issues, and collaborate on platform enhancements and operational best practices.

Build and maintain Quora's ML platform and ranking infrastructure to serve large-scale ML models. Work on GPU model serving, distributed systems, developer tooling, feature store modernization, and on-call rotation to ensure high availability, performance, and cost efficiency.