Top Remote Machine Learning Engineer Jobs

Leads the design, development, and deployment of AI and machine learning solutions for healthcare technology platforms. Builds scalable ML models, MLOps pipelines, LLM and RAG systems, backend services, and agent-based applications. Establishes standards for monitoring, testing, CI/CD, and lifecycle management while collaborating with engineering, data, product, security, and SRE teams. Provides architectural guidance and technical mentorship, evaluates emerging AI technologies, and promotes responsible, secure, and resilient AI implementation.

Leads the design, development, deployment, and operationalization of enterprise AI solutions, including LLM applications, RAG systems, intelligent agents, classification models, and decision-support tools. Establishes prompt strategies, guardrails, evaluation frameworks, monitoring, observability, and governance practices. Partners with Finance, Data Engineering, Architecture, Security, and Responsible AI teams to deliver scalable, secure, compliant, and production-ready solutions.

Design, develop, and operate scalable VoIP infrastructure supporting AI-driven voice workloads. Integrate telephony systems, LLM applications, and real-time communication platforms while improving observability, reliability, deployment, and SRE practices. Build tested, maintainable software across telephony and AI layers, support production environments, collaborate with engineering and product teams, and mentor colleagues.

Develop and operate scalable VoIP infrastructure supporting AI-driven voice workloads. Build telephony platforms, observability features, and LLM integrations across SIP/RTP, Kamailio, RTPEngine, and related systems. Collaborate with engineering, product, infrastructure, and SRE teams on deployments, production support, automation, testing, and platform reliability. Mentor colleagues and promote engineering best practices across telecom and AI disciplines.

Lead machine learning architecture and technical direction across multiple quarters while remaining involved in experimentation, quality, and production systems. Work across model post-training, personalization and recommendation, and agentic experiences. Define solutions for ambiguous problems, align cross-functional partners, drive execution, and safely bring ML systems into production.

Design, develop, and deploy secure AI/ML and full-stack solutions for healthcare underwriting and benefits modernization. Build RAG pipelines, integrate LLMs and agentic AI capabilities, develop Python-based machine learning systems, and expose models through production APIs. Collaborate with engineering, architecture, product, data, and underwriting teams while ensuring model availability, security, performance, testing, monitoring, and responsible AI use.

Lead the technical vision and development of scalable ML and AI evaluation systems for Atlassian’s Rovo Chat. Build agentic capabilities, datasets, simulations, automated judges, experimentation platforms, and quality signals. Identify failure modes, ship interventions, and improve customer outcomes while balancing quality, latency, reliability, safety, and cost. Provide cross-organizational technical leadership, mentorship, design guidance, and strategic direction for AI products.

Principal Machine Learning Engineer responsible for setting technical direction and building production-grade AI systems for Confluence. The role spans model evaluation, retrieval, ranking, prompt and workflow design, experimentation, reliability, and customer-facing AI product development. Responsibilities include making architectural decisions, partnering across engineering and product teams, mentoring engineers, identifying model and product failure modes, and delivering scalable AI experiences across content creation, editing, discovery, recommendations, and multimodal interaction.

Lead design and deployment of production-grade ML systems (ranking, retrieval, LLMs). Drive architecture decisions, run experiments and evaluations, build scalable feature pipelines and monitoring, mentor engineers, and collaborate cross-functionally to integrate AI into products and scale ML capabilities.

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.

Leads architecture and hands-on delivery of production-grade software, machine learning, generative AI, RAG, and AI-agent solutions. Builds cloud-native services, APIs, data integrations, MLOps/LLMOps pipelines, evaluation frameworks, and secure deployment patterns. Owns technical initiatives from experimentation through production, including reliability, observability, testing, incident resolution, responsible AI, and cost optimization. Influences cross-team architecture and roadmaps while mentoring engineers without direct management responsibilities.

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.

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.

Build and operate Philo’s ML and LLM infrastructure, including AWS and Kubernetes platforms, GPU scheduling, model serving, inference optimization, monitoring, evaluation, experimentation, and CI/CD. The role establishes infrastructure best practices, reliability, security, scalability, and Infrastructure as Code while supporting recommendation, video classification, and agent workloads. It also involves collaborating with engineers and vendors to deploy, instrument, and manage production systems.

Design, develop, train, optimize, and deploy machine learning models, including NER, personalization, predictive, and LLM fine-tuning systems. Collaborate with engineering, product, and analytics teams to integrate AI features into Atlassian products. Conduct experimentation, evaluate model performance, monitor product metrics, and improve scalability and reliability. Contribute to foundational model development and advanced AI initiatives while communicating findings to senior leaders.

Build and lead production machine learning and evaluation systems for Atlassian’s Rovo AI agents. Responsibilities include developing datasets, simulations, automated judges, experimentation platforms, and quality signals; advancing reasoning, planning, retrieval, memory, and tool use; identifying and correcting systemic failures; and balancing quality, safety, latency, reliability, and cost. The role requires hands-on technical leadership, cross-functional influence, architectural decision-making, mentorship, and delivery of measurable customer outcomes at scale.

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.

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.

Develop and deploy machine learning, generative AI, agentic AI, machine vision, IIoT, predictive maintenance, and process-monitoring solutions for manufacturing operations. Build Azure data pipelines, manage models through production, establish governance standards, integrate systems, and support plant implementations. Collaborate with cross-functional teams, provide technical guidance and training, evaluate emerging technologies, and manage project scope, schedules, budgets, vendors, and change orders. Travel to Wood Products facilities is required, with approximately 25% travel for employees based outside the area.

Design and build production-grade AI memory systems, including retrieval pipelines, storage tiers, privacy filtering, memory extraction, adaptive updates, hybrid search, and evaluation frameworks. Ship and maintain LLM-powered features, diagnose RAG and prompt failures, implement agentic workflows and tool integrations, and ensure reliable degradation and fallback strategies. Collaborate with product management to define feasible memory-system capabilities while owning production reliability.

Build and scale pipelines that transform conversation data into structured customer memory. Develop LLM extraction and retrieval systems using embeddings, vector search, and ranking; create evaluation benchmarks and regression gates; diagnose production issues through logs, metrics, and traces; and partner with Product and ML teams to improve customer-focused AI systems.

Build and optimize scalable pre-training pipelines for foundation models. Implement distributed training across GPUs, TPUs, and high-performance clusters; translate research experiments into production workflows; develop monitoring and fault-tolerance systems; and benchmark and tune performance across hardware and software stacks.