Top Remote Machine Learning Engineer Jobs

Leads the architecture and development of production-grade Generative AI, RAG, agentic, and intelligent automation solutions. Builds LLM applications, APIs, retrieval systems, evaluation frameworks, and cloud-based AI workloads using Python and modern data platforms. Establishes practices for testing, monitoring, governance, security, reliability, and responsible AI. Partners with clinical, operational, product, and business stakeholders while mentoring engineers and evaluating emerging technologies.

Designs and deploys enterprise AI/ML solutions, scalable models, data pipelines, and MLOps architectures in cloud production environments. Responsibilities include model monitoring and optimization, responsible AI implementation, workflow automation, emerging technology evaluation, cross-functional architecture collaboration, code reviews, and mentoring. The role focuses on healthcare challenges and supports production-grade machine learning systems, including real-time inference and continuous integration and deployment.

Lead the architecture and development of large-scale machine learning systems, distributed training infrastructure, ranking systems, and production ML services. Drive complex initiatives from concept through deployment, establish engineering standards, partner with product and engineering leadership, and mentor data scientists and engineers. Build reliable, low-latency, high-availability AI platforms using advanced ML frameworks, accelerators, and big data technologies.

Build scalable systems and machine learning models that transform satellite imagery into labeled geospatial data and production computer vision products. Responsibilities include developing data and annotation pipelines, tracking lineage and versioning, deploying inference services, evaluating models, supporting cloud infrastructure, and improving labeling workflows. The role requires strong Python, cloud, containerization, CI/CD, infrastructure-as-code, production support, and deep learning expertise, with preferred experience in GCP, Vertex AI, spatial databases, and satellite imagery.

Designs, develops, tests, validates, and deploys AI/ML models across generative AI, classification, prediction, computer vision, NLP, and reinforcement learning. Builds data and model-training pipelines, leverages cloud platforms, evaluates model performance, documents methodologies, collaborates with technical teams, and guides prototype-to-production transitions. Requires five years of relevant experience and an active TS/SCI clearance with polygraph.

Design, train, fine-tune, optimize, and deploy large-scale machine learning systems for cloud security use cases. Build end-to-end ML pipelines, develop transformer and embedding models, productionize open-weight language models, and optimize inference for latency, cost, and quality. Architect resilient ML services across AWS and GCP using cloud-native microservices while collaborating with engineering teams on AI strategy and solving complex problems involving massive datasets.

Own the strategy, architecture, development, deployment, monitoring, and continuous improvement of production personalization and recommendation systems. Build custom embeddings, ranking models, and shared representations across feeds, search, and recommendations. Lead ambiguous problems from concept to measurable customer impact, establish AI evaluation standards, partner with product and data teams, make cross-team technical decisions, and coach senior engineers.

Lead the design and delivery of knowledge graph and machine learning systems that infer personal work context from connected tools. Build graph inference pipelines, schemas, permission-aware APIs, Rovo Chat integrations, and MCP-compatible CLI experiences. Establish evaluation frameworks, improve retrieval and grounding, provide technical direction across teams, mentor engineers, and ensure responsible, privacy-safe AI systems operate reliably at production scale.

Develop and implement advanced machine learning algorithms and models for Atlassian products. Design system and model architectures, conduct experiments and evaluations, collaborate with product, engineering, and analytics teams, and mentor emerging machine learning engineers. The role also contributes to shared AI infrastructure and capabilities supporting search, knowledge discovery, and conversational experiences across the company.

Design, deploy, and scale production AI/ML solutions for healthcare, including predictive models, generative AI, statistical analysis, microservices, APIs, and MLOps pipelines. Analyze complex clinical datasets, implement model monitoring and governance, evaluate emerging AI technologies, and collaborate with engineering, data, product, and clinical teams to improve care delivery and operational efficiency.

Build and operate production machine learning systems enabling autonomous medical coding at scale. Responsibilities include data discovery, feature engineering, model training and evaluation, deployment, monitoring, MLOps pipelines, LLM integration, observability, governance, fairness assessment, and A/B testing. The role also involves designing highly available low-latency architectures, defining technical roadmaps, and collaborating with product, design, data, and platform teams.

Leads enterprise AI application architecture and hands-on development for LLM, agentic, multi-agent, and RAG solutions. Establishes scalable design patterns, governance standards, observability, reliability, and compliance practices. Collaborates with product, engineering, and compliance teams, evaluates emerging AI technologies, optimizes performance, and mentors senior engineers. The role requires extensive software engineering and enterprise AI architecture experience, with expertise in cloud-native systems, Kubernetes, CI/CD, MLOps, and production AI platforms.

Build and operate production LLM-powered agents and machine learning systems for customer risk identification and threat detection. Translate security heuristics into agent logic, collaborate with threat researchers, develop data requirements and pipelines, evaluate precision and recall, and deploy solutions through CI/CD. The role also requires production reliability ownership, observability, incident debugging, cloud infrastructure expertise, and senior staff-level technical leadership.

Build and deploy enterprise agentic AI solutions, including agents, MCP tools, plugins, orchestration architectures, evaluation harnesses, and secure APIs. Integrate agents with search and collaboration platforms, instrument production behavior, manage cost and latency, and test against prompt injection and tool vulnerabilities. The role requires backend software engineering, production agent development, MCP experience, LLM evaluation, and AI coding assistant expertise, with preferred experience in managed runtimes, retrieval systems, and enterprise security.

Lead development and deployment of an agentic AI/ML pipeline for identifying, prioritizing, and pricing high-cost healthcare claims. Responsibilities include model development, confidence scoring, HITL safety design, ML lifecycle management, monitoring, workflow automation, responsible AI, and enterprise-scale analytics using Databricks and Snowflake. The role also evaluates emerging AI technologies and supports regulatory-aware automation and healthcare data transformation.

Designs, deploys, and scales AI and machine learning solutions for enterprise healthcare applications. Responsibilities include building and tuning production models, developing MLOps frameworks and deployment pipelines, monitoring model performance, automating workflows, evaluating emerging AI technologies, and ensuring security, scalability, responsible AI, and governance standards. The role collaborates with software engineers, data scientists, and business stakeholders.

Designs, develops, deploys, and supports production AI/ML solutions for healthcare operations. Builds scalable machine learning services, data pipelines, cloud applications, and MLOps workflows; implements CI/CD, monitors and optimizes deployed systems, evaluates emerging AI technologies, and collaborates with product, architecture, data science, and engineering teams. The role also mentors junior engineers and contributes to technical standards, code reviews, and architecture decisions.

Leads the design, development, deployment, and operationalization of enterprise AI/ML, generative AI, and automation solutions. Provides technical leadership across architecture, engineering practices, evaluation, monitoring, governance, security, and responsible AI. Partners with cross-functional stakeholders, mentors engineers, establishes reusable standards, evaluates production readiness, and ensures scalable, reliable, compliant, and cost-effective delivery of AI/ML capabilities.

Leads discovery, technical scoping, architecture, development, deployment, and optimization of production AI/ML software. Mentors engineering pods, evaluates emerging AI technologies, collaborates with data scientists and product teams, and ensures model and pipeline reliability, scalability, and performance. The role also promotes responsible AI, engineering best practices, automation, and technical standards.

Design, deploy, and scale responsible AI and machine learning solutions supporting healthcare provider experiences. Lead AI initiatives, provide technical direction to AI engineers, evaluate emerging technologies, and partner with product, data, and business teams. Ensure models meet enterprise standards for performance, security, governance, and scalability while contributing to cloud modernization. The role emphasizes generative AI, LLMs, conversational AI, API development, cloud hosting, and production operations.

Leads enterprise AI/ML architecture and engineering strategy, developing scalable AI-powered applications, agentic and generative AI solutions, streaming data pipelines, and cloud-native platforms. The role uses Python, Java, Spring Boot, Next.js, Kafka, and PostgreSQL while establishing architecture standards, governance, security, and reliability practices. Responsibilities include technical leadership across programs, stakeholder influence, mentoring engineering teams, evaluating emerging technologies, and driving responsible AI adoption.

Develop and productize machine learning and digital signal processing algorithms for speech and audio applications, including noise suppression, echo cancellation, beamforming, spatial audio, and enhancement. Research modern deep learning approaches, prototype with Python or MATLAB, and deploy optimized real-time solutions on consumer hardware. Collaborate across research, systems engineering, and product development to deliver robust audio technologies at scale.

Lead end-to-end machine learning initiatives for Block’s conversational support systems. Responsibilities include developing and maintaining chatbot and recommendation models, researching LLM, RAG, fine-tuning, and real-time inference architectures, shaping long-term ML roadmaps, and guiding cross-functional teams. The role also requires communicating technical strategy and outcomes to leadership and external stakeholders while delivering scalable, production-ready AI solutions across Cash App, Square, and other Block products.

Leads end-to-end machine learning initiatives for Block’s support experiences, including conversational AI, chatbots, recommendation systems, and agent tools. Defines ML roadmaps, guides research into LLMs, RAG, fine-tuning, and real-time inference, and designs, deploys, and maintains production models. The role requires strategic technical leadership, cross-functional influence, stakeholder communication, and extensive experience delivering applied ML and generative AI systems.

Leads the design, development, deployment, and scaling of production AI/ML solutions for healthcare. Builds cloud-based machine learning systems, data pipelines, feature workflows, and MLOps processes including CI/CD, monitoring, drift detection, retraining, and governance. Provides architecture guidance, technical leadership, mentorship, and cross-functional partnership while applying NLP, deep learning, computer vision, foundation models, and other advanced AI techniques.