Top Remote Machine Learning Jobs

Lead and grow a team of ML engineers building applications for a graphically intensive simulation platform. Manage stakeholders, translate customer needs into technical requirements, prioritize between off-the-shelf, open-source, and greenfield solutions, coordinate cross-functional delivery, and oversee the full ML lifecycle from data infrastructure to deployment. Foster team ways of working and performance.

Design, develop, and deploy machine learning models and pipelines to optimize operations. Lead full ML project lifecycle, ensure model evaluation/monitoring, collaborate cross-functionally, mentor junior engineers, and drive continuous improvement in ML applications and processes.

Leads and manages an applied machine learning team developing fraud detection and identity verification models. Responsibilities include people management, coaching, code review, production ML systems, model lifecycle ownership, experimentation, deployment, monitoring, fraud research, cross-functional prioritization, and communicating results. The role remains hands-on while partnering with Product, Engineering, Risk, and leadership to drive measurable business impact and shape future product strategy using machine learning and AI.

Leads an applied machine learning team and owns fraud detection and identity model strategy, development, deployment, monitoring, and iteration. Partners with Product, Engineering, Risk, and senior leadership to set priorities and deliver business impact. Provides technical mentorship, reviews code, guides modeling and architecture decisions, researches fraud patterns, develops ML capabilities, and shapes responsible AI use under data governance and security constraints.

Lead the architecture and end-to-end development of production-grade AI/ML systems, including training, deployment, monitoring, MLOps, deep learning, NLP, ASR, and agentic AI workflows. Translate research into scalable enterprise solutions, establish engineering and governance standards, evaluate emerging tools, communicate technical tradeoffs to leadership, and mentor engineers. The role requires expertise in Python, deep learning frameworks, cloud ML platforms, ML pipelines, statistics, and production LLM or agentic systems.

Leads an AI/ML engineering team building and operating enterprise intelligence capabilities, including LLM workflows, RAG pipelines, agentic systems, copilots, classifiers, and evaluation frameworks. Establishes architecture, safety guardrails, testing, observability, governance, and production-readiness standards. Partners with data engineering, security, privacy, compliance, and architecture teams while mentoring engineering talent and driving strategic innovation.

Design and operate scalable batch and streaming data platforms supporting machine learning and generative AI. Build pipelines for structured, unstructured, OCR, document, and image data; develop RAG, semantic search, and LLM-powered solutions; and establish data quality, observability, governance, orchestration, and deployment practices. Partner with stakeholders, lead platform scalability and cost optimization, mentor engineers, and translate ambiguous needs into production-ready technical roadmaps while securely handling sensitive data.

Build and deploy agentic and generative AI systems that automate risk and compliance investigations. Own full-stack ML systems, including data integrations, prompt tuning, orchestration, evaluation, productionization, monitoring, and transformer-based modeling across transaction, graph, text, and profile data. Collaborate with product, engineering, platform, and operations teams while leading strategic roadmaps and delivering scalable production systems.

Lead design, development, and deployment of end-to-end recommendation systems. Build, tune, and evaluate scalable recommendation algorithms, integrate ML into products, lead projects, mentor teams, and communicate solutions to clients and stakeholders while driving innovation in replenishment and supply chain analytics.

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.

Design and deploy machine learning models for financial document processing, optimize large language models, collaborate with cross-functional teams, and guide junior peers in a fast-paced environment.

Develop, validate, and deploy biomedical ML and deep learning algorithms on sensor data. Own end-to-end lifecycle from data curation to embedded/cloud deployment, run clinical study design and statistical validation, and collaborate with cross-functional R&D, firmware, and software teams.

Architect, deploy, and maintain production-grade AI and machine learning systems across GenAI and classical ML projects. The role owns technical architecture, monitoring, alerting, model lifecycle management, cloud deployment, MLOps practices, and continuous improvement. Responsibilities also include guiding project teams, making strategic technology decisions, translating AI concepts into business value, evaluating practical applications of research, and communicating technical recommendations to engineering and business stakeholders.

Develop and automate Python-based ETL and data pipelines in Databricks; clean and preprocess manpower/personnel data; build, train, and deploy machine learning models; create Qlik/Tableau reports and dashboards; maintain historical data and troubleshoot ETL; produce documentation and data products; collaborate with cross-functional teams to translate requirements into data-driven solutions.

Build and deploy machine learning models on large healthcare datasets, extract actionable insights, develop scalable data processing systems, collaborate with cross-functional teams, and communicate findings to stakeholders to improve patient outcomes.

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.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

The role involves conducting research and optimizing machine learning systems while collaborating with technical staff to enhance product performance.

Lead development of motion planning and prediction ML models for Level 4 autonomous vehicles. Design and productionize behavior models using deep learning, generative AI, and reinforcement learning. Deploy solutions to vehicle fleets, collaborate with perception, simulation, data platform and integration teams, and report progress to leadership.

Lead the sports-content product owning real-time low-latency data pipelines, proprietary ML models, fan LTV and identity, rights compliance, AI-driven highlights, sponsorship analytics, and senior client/commercial relationships to deliver sub-500ms actionable intelligence for betting, broadcast, and rights customers.

Develop and implement machine learning and optimization algorithms to improve business KPIs. Analyze large datasets, produce production-quality software components, design experiments, and collaborate with teams to improve data collection, modeling, and scalable systems.

The role requires a skilled Machine Learning Engineer to create complex models and AI solutions while collaborating with teams to enhance products and services.

Design, develop, and deploy end-to-end machine learning and large language model solutions. Perform data discovery, cleaning, preprocessing, and visualization. Build/train LLMs and Agent+RAG systems, collaborate with stakeholders and AI teams, and apply graph-based analysis to support enterprise integration and analytics.

Design, build, and operate production ML decision systems to detect and prevent payment fraud, account takeover, scams, and other abuse. Integrate diverse signals into low-latency serving and batch scoring, own feature pipelines and model lifecycle, develop AI-assisted triage and feedback loops, and partner cross-functionally to balance fraud reduction with legitimate customer access.

Develop and deploy LLM workflows and production machine learning models for nuclear-system design and analysis. Build surrogate models for real-time digital twins, manage data pipelines, maintain rigorous information controls, and lead AI initiatives from inception through delivery. The role also involves growing a software engineering team, presenting roadmaps and demos to leadership, gathering user feedback, and collaborating with nuclear engineers in a fast-paced startup environment.