Top Remote Machine Learning Engineer Jobs

As a Staff Machine Learning Engineer, lead teams in designing AI solutions, managing projects, and interfacing with clients while ensuring technical excellence and operational effectiveness.

Lead design and development of scalable, high-performance ML training infrastructure. Drive distributed training performance optimization, observability, and developer experience. Own cross-functional infrastructure initiatives, set technical direction and standards, and mentor engineers to deliver platform capabilities that support large-scale model training.

Lead technical vision and architecture for ML-driven trajectory generation in autonomous vehicles. Build and deploy scalable training pipelines, integrate models into real-time safety-critical onboard systems, mentor senior engineers, drive cross-functional initiatives, and move solutions from research to production using simulation and large-scale datasets.

Develop and evaluate ML and perception models for future sensing and autonomous driving. Work on detection, tracking, classification, sensor fusion, training pipelines, validation, robustness metrics, and integrate models into onboard systems while collaborating with cross-functional teams.

Lead end-to-end ML and perception efforts for future sensing in autonomous vehicles: design sensor architectures, multi-modal fusion, model development, metrics, simulation-driven evaluation, system integration, and production readiness while mentoring engineers and building scalable data and training infrastructure.

Lead design, development, and enterprise deployment of scalable production ML/AI solutions for pharmacy services. Own MLOps pipelines, model training, validation, containerized deployment, monitoring, and architecture. Collaborate with data scientists, engineers, and business leaders, evaluate emerging AI/LLM technologies, and provide technical leadership and mentorship.

The Senior AI ML Engineer will design and implement AI/ML systems for healthcare, manage projects from ideation to release, and mentor engineers. Responsibilities include delivering quality code, driving architectural decisions, and collaborating with cross-functional teams.

Lead end-to-end ML systems for identity verification, including document authenticity, face match, liveness and deepfake detection. Build GNN-based identity graphs, behavioral/device-intelligence models, vendor benchmarking/evaluation, and production enforcement. Mentor engineers and align ML architecture with Product, Compliance, Risk, and Security stakeholders.

Lead architecture and productionization of a multi-agent, LLM-based AI platform for identity security. Design agent orchestration, context/memory management, and scalable, low-latency infrastructure. Transition prototypes to hardened enterprise pipelines, set reliability and evaluation frameworks for non-deterministic AI, and mentor senior engineers while partnering with product and security leadership.

Lead technical strategy and multi-year roadmap for search and vector infrastructure powering Atlassian AI. Drive cross-team initiatives for semantic search, agentic retrieval, and RAG; ensure scalable, cost-effective ML serving with strong reliability and FinOps discipline; mentor senior engineers and raise organizational engineering craft.

Lead development and operation of CI platform components (RBE, FUSE) to enable scalable build, test, and developer workflows. Collaborate across teams, own projects end-to-end, drive engineering best practices, mentor engineers, perform design/code reviews, and support integration of internal tools with the FUSE-based file system to improve developer productivity for AV and ML workloads.

Design, build, and deploy scalable AI/ML solutions and end-to-end ML pipelines (ingestion to deployment). Implement MLOps/LLMOps, RAG/LLM applications, agentic workflows, model monitoring, and integrate solutions into production using cloud CI/CD.

Design, develop, and deploy production agentic AI systems and multi-agent orchestration for conversational healthcare. Build FastAPI and Node.js/TypeScript backends integrating LLMs (Bedrock, Vertex) via GraphQL/AppSync. Implement PII scrubbing, prompt safety, and compliance controls. Deploy containerized workloads to AWS using Terraform and CI/CD. Collaborate with product and clinical stakeholders, monitor production LLM performance and user metrics, and apply responsible AI practices.

Build and maintain core ML infrastructure and platforms enabling model development, training, deployment, monitoring, and MLOps. Partner with product teams to design scalable, fault-tolerant ML systems, fine-tune LLMs, curate datasets, and drive production launches while mentoring engineers.

Design, build, and support high-throughput, low-latency LLM inference and data pipelines; collaborate with data scientists to post-train and evaluate models; implement scalable, production-ready systems with strong testing, monitoring, and performance focus; mentor engineers and iterate on architecture, reliability, and user-facing AI applications.

As an ML Ops Infrastructure Engineer, you will design CI/CD pipelines for ML, maintain deployment systems, and implement monitoring while collaborating with research teams to ensure model quality and performance.

The engineer will build and operate AI/ML infrastructure, managing services on AWS and bare metal, using tools like Kubernetes and Terraform.

Lead and grow an ML engineering team responsible for the end-to-end ML lifecycle: data collection, preprocessing, model development, deployment, evaluation and monitoring. Research and implement scalable ML and generative AI techniques (recommendation and agentic systems), mentor and hire engineers, drive pragmatic, business-focused solutions, and communicate technical results to stakeholders.

As a Senior Machine Learning Engineer, you will lead model validation for AI systems, challenge model soundness, and build validation tools for high-stakes areas such as credit and fraud prevention.

Customer-facing, hands-on AI builder who shapes strategic pursuits, designs end-to-end AI/GenAI architectures, prototypes demos and proofs-of-value, mentors solution leads, supports RFPs, and partners with engineering for implementation and delivery rotations.

The Sr. Machine Learning Engineer will develop and deploy ML solutions for healthcare, manage data pipelines, and work with large datasets to enhance healthcare delivery.

Build and deploy scalable ML training and evaluation platforms, drive technical projects and architectural decisions, collaborate with partner teams, prioritize platform improvements, ensure reliability and performance, and mentor junior engineers.

Design, implement, and deploy scalable ML training and evaluation platforms. Own end-to-end projects, make architectural decisions, drive prioritization, collaborate cross-functionally, and mentor engineers to accelerate autonomous driving model development.

The role involves optimizing large-scale ML infrastructure for autonomous vehicles, conducting analyses, collaborating with teams, and improving system performance.

Lead architecture and technical strategy for AI-driven product quality systems using LLMs and agents. Build scalable evaluation frameworks, detect regressions, generate insights, and drive cross-functional adoption while mentoring engineers and defining standards for trustworthy AI.