Top Remote Machine Learning Engineer Jobs

Design, deploy, and operate production agentic AI systems for healthcare applications. Build multi-agent orchestration, backend services, LLM integrations, healthcare guardrails, and cloud infrastructure. Collaborate with product and clinical stakeholders to evaluate workflows, improve performance, and monitor metrics such as latency, token usage, and user satisfaction. Apply responsible AI practices and support systems serving millions of health plan members.

Lead the design and implementation of AI initiatives for edge devices. Develop and optimize machine learning solutions using large-scale sensor, video, text, diagnostic, transaction, and location data. Partner with engineering and business teams to prototype AI experiences, ship production systems, adopt research advances, and improve edge model performance. Mentor ML engineers and communicate technical concepts to varied audiences.

Build full-stack applications and AI/ML solutions using Java, React, APIs, Kafka, MySQL, Kubernetes, and cloud platforms. Design and deploy LLM and generative AI capabilities, implement CI/CD pipelines, monitor production systems, write automated tests, address security and quality issues, and participate in architecture, code reviews, product lifecycle management, and agile ceremonies.

Develops end-to-end AI features for Atlassian’s developer AI products, including LLM integration, prompt engineering, machine learning algorithms, data preprocessing, feature engineering, experimentation, evaluation, pipeline optimization, and monitoring. Collaborates with data scientists, software engineers, and partner teams to solve complex problems and apply advances in AI and machine learning.

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.

Leads the design, development, and delivery of production machine learning and AI systems for government and commercial customers. Responsibilities include data shaping, analytics, model development and optimization, production deployment, technical planning, customer engagement, and mentoring cross-functional engineering teams. The role requires translating complex technical concepts for internal and external stakeholders and traveling up to 25% of the time.

Leads applied AI research and builds production-grade healthcare AI systems, including LLM applications, agentic workflows, RAG pipelines, and machine learning services. Partners with executive stakeholders, evaluates models and architectures, establishes automated testing and monitoring, and owns systems from experimentation through reliable operation. Embeds security, privacy, responsible AI, access controls, and human oversight. Sets technical direction, creates reusable platforms and standards, mentors engineers, and delivers solutions across ambiguous, enterprise-wide initiatives.

Build and deploy production-grade AI/ML software, APIs, and applications for healthcare provider use cases. Develop prompt-based AI capabilities, integrate enterprise data and cloud services, create automated tests and evaluation pipelines, and ensure security, observability, scalability, and reliability. Collaborate with product, architecture, data science, and platform teams while owning technical work from proof of concept through production.

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.

Build and scale production machine learning solutions for lottery fulfillment. Develop and optimize computer vision and multimodal models, fine-tune and evaluate models, create data pipelines and evaluation frameworks, and deploy models on cloud infrastructure. Integrate models into real-time systems involving image processing, barcode decoding, and OCR. Collaborate cross-functionally, improve engineering standards, mentor teammates, and support reliable nationwide expansion.

Leads software engineering for AI/ML products within an agile banking technology team. Develops, reviews, debugs, and operationalizes secure production code using Python, PySpark, Databricks, AWS, and LLM technologies. Establishes governance and validation standards for AI-assisted engineering, promotes responsible AI practices, guides technical design and operations, and serves as a subject matter expert across teams.

Design, build, and operate scalable hybrid human/machine data labeling platforms for autonomous vehicles. Own end-to-end projects, define platform roadmap, integrate ML-driven annotation, build high-performance UIs/APIs, and collaborate across ML, product, and operations to improve labeling quality and velocity.

Lead the design and hands-on development of an enterprise AI/ML lifecycle governance platform. Build integrations connecting intake, governance gating, routing, observability, monitoring, alerting, feedback, and containment. Define data flows and technical contracts, develop production-grade Python services, implement automated testing, and guide cross-functional engineering teams. The role also evaluates cloud-native platforms, AI observability, governance automation, and responsible AI practices.

Develop agentic workflows and automation for real-time audio machine learning research. Responsibilities include orchestrating training, evaluation, experimentation, reporting, data preparation, hyperparameter searches, benchmarking, regression testing, experiment tracking, artifact management, and reproducible execution. The role integrates LLM agents with research tools, datasets, repositories, evaluation suites, and GPU infrastructure while partnering with audio researchers to create reusable workflows and dashboards.

Develops core AI capabilities for Atlassian’s DevAI products, including LLM-based methodologies, prompt engineering, end-to-end AI features, data preprocessing, feature engineering, experimentation, model evaluation, pipeline optimization, and monitoring. Collaborates with data scientists, software engineers, and cross-functional partner teams to solve complex problems and apply advances in machine learning and AI.

Develop, train, evaluate, optimize, and debug machine learning models for consumer audio applications. Focus on efficient AI and Edge ML by reducing latency, memory usage, and computational demands. Collaborate with software, embedded systems, and hardware engineers to deploy models on resource-constrained devices. Explore techniques such as quantization, pruning, and model compression while contributing to emerging AI technologies and practical product development.