Top Remote Machine Learning Jobs

Lead end-to-end machine learning projects to improve customer engagement and retention. Build, test, and deploy models for user forecasting, personalization, and Casino engagement. Partner cross-functionally to scale solutions, mentor junior data scientists, and communicate model impact to influence product and marketing strategy.

Intern will own and deliver a scoped engineering project on Deepgram's voice platform (ASR/TTS/LLM/API/SDK), ship reviewed code to staging/production, use AI in development workflows, improve a component measurably, and present results with documentation.

Lead and direct multidisciplinary teams to research, develop, test, integrate, and demonstrate advanced signal processing, optimization, and machine learning algorithms for radar, electronic warfare, and sensor systems. Oversee modeling/simulation, algorithm T&E, customer engagement, and technical briefings for defense and intelligence applications.

Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

As a Principal Machine Learning Systems Engineer, you will lead the development and implementation of machine learning systems and solutions to enhance team collaboration and productivity across Atlassian's software products.

Lead the design, development, and deployment of advanced machine learning solutions at Optum Tech, mentoring junior engineers and collaborating across teams.

Partner with healthcare clients to discover, design, and deliver production-grade AI/LLM solutions. Architect cloud-native systems, build and deploy ML/LLM applications, prototype rapidly, troubleshoot distributed systems, and lead client engagements to drive measurable business outcomes while collaborating across product, platform, and engineering teams.

As a Staff Machine Learning Engineer, you'll enhance credit products through advanced modeling techniques, data analysis, and ML operations, supporting robust decision-making processes.

As a Staff Machine Learning Engineer, you will enhance credit modeling, manage full credit modeling stacks, and support innovations within regulated banking frameworks.

Technical lead for end-to-end ML and CV pipelines to reconstruct, validate, and maintain map primitives from multi-modal vehicle sensor data. Architect scalable offline and production map reconstruction systems, define technical strategy, drive cross-functional integration, mentor senior engineers, and ensure robustness, safety, and operational scalability for national-scale mapping.

Develop and deploy machine learning solutions for autonomous vehicles, mentoring engineers and collaborating on model implementations.

Lead a team of ML engineers to develop fraud detection models, defining strategies, driving model evolution, and ensuring effective integration with cross-functional teams.

The Staff ML Engineer will design and operate Samsara's ML platform, collaborating with teams to enhance ML features and improve safety outcomes. Responsibilities include overseeing system reliability, leading technical direction, and mentoring engineers.

Build and own fraud metrics, dashboards, and reporting. Design and implement real-time monitoring, alerts, and predictive ML-driven risk controls. Balance loss savings with customer friction, run experiments (A/B tests), partner with GTM teams, summarize findings to stakeholders, and assist teams with fraud triage.

Design, build, and deploy production-grade ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM applications, and scalable data pipelines with observability and MLOps practices for cloud and on-prem environments.

Lead design and delivery of scalable Amazon Connect voice and chat solutions. Build Lambda-based integrations, integrate Lex bots and external systems, optimize routing/queues, ensure high availability and monitoring, lead production support, RCA, and continuous improvement for contact center platforms.

Lead design, build, and operate end-to-end AI/ML-enabled full-stack applications for clinical workflows. Develop Next.js/React frontends, Node.js backends, PostgreSQL data models, integrate LLM capabilities, deploy and monitor on Azure, implement CI/CD and security best practices, and mentor engineers.

Lead AI/ML engineering to convert proofs-of-concept into production-grade, enterprise AI/SaaS systems. Drive LLM optimization, RAG and agentic workflows, cloud deployments, model monitoring, ethical and compliant AI, stakeholder engagement, and talent development while embedding with clients and scaling solutions for reliability, performance, and cost-effectiveness.

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 a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

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.

Drive technical strategy for the Personalization Platform, develop recommendation systems, maintain ML workflows, and innovate AI solutions for customer engagement.

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.

As a Staff Applied ML Scientist, you will develop and maintain fraud detection models, conduct research on fraud types, and collaborate across teams to drive product development and real-time decision-making.