Top Remote Machine Learning Engineer Jobs

Lead DigitalOcean’s AI security strategy, governance framework, roadmap, and organizational programs. Build and operationalize AI/ML security controls, tooling, guardrails, and automation; conduct threat modeling, adversarial testing, and AI red teaming. Review AI architectures, guide high-risk product decisions, monitor deployed models, and address risks involving RAG, agents, model supply chains, and prompt injection. Represent AI security to executives and external communities while translating emerging research and threats into engineering guidance and policy.

Lead DigitalOcean’s AI security strategy, governance framework, and multi-year roadmap. Secure AI/ML systems across data ingestion, training, model serving, RAG, agents, and monitoring. Conduct threat modeling, adversarial testing, AI red teaming, and security reviews; develop guardrails and automation with engineering teams. Advise executives and the board on AI risk, define organizational controls, and guide responses to emerging threats including prompt injection, model supply-chain risks, and AI-enabled social engineering.

Lead DigitalOcean’s AI security strategy, governance framework, and multi-year roadmap. Secure AI/ML systems across data ingestion, training, model serving, RAG, agents, and monitoring. Conduct AI red teaming, threat modeling, security reviews, and adversarial testing, while building defensive tooling and guardrails. Advise executives and the board on AI risk, guide engineering teams, and define organizational policies for responsible AI security.

Lead DigitalOcean’s AI/ML security strategy, architecture, governance, and multi-year roadmap. Build security controls, guardrails, tooling, and automations; oversee AI red teaming, adversarial testing, threat modeling, and model monitoring. Review AI product architectures, guide high-risk decisions, establish responsible AI policies, and brief executives and the board on AI security risks. Serve as the internal expert and external representative on emerging AI security topics, including agentic AI, model supply chains, prompt injection, and data integrity.

Lead technical direction for high-impact machine learning initiatives, from research and experimentation through production deployment. Build and fine-tune LLMs, document understanding systems, model-serving infrastructure, evaluation frameworks, and data feedback loops. Establish standards for model quality, calibration, reproducibility, and responsible deployment while mentoring ML engineers and influencing organizational roadmaps. The role requires hands-on software and ML engineering expertise, production-scale model development, and strong statistical judgment.

Lead technical direction and people management for an AI/ML engineering squad. Design, build, and deploy production-grade ML and Generative AI solutions (LLMs, RAG, agentic systems), drive MLOps/CI-CD, ensure Responsible AI and HIPAA-compliant practices, conduct architecture/code reviews, and mentor engineers to deliver measurable business outcomes.

As a Staff Machine Learning Engineer, lead teams in designing AI solutions, managing projects, and interfacing with clients while ensuring technical excellence and operational effectiveness.

Develop and evaluate ML and perception models for future sensing and autonomous driving. Work on detection, tracking, classification, sensor fusion, training pipelines, validation, robustness metrics, and integrate models into onboard systems while collaborating with cross-functional teams.

Lead technical vision and architecture for ML-driven trajectory generation in autonomous vehicles. Build and deploy scalable training pipelines, integrate models into real-time safety-critical onboard systems, mentor senior engineers, drive cross-functional initiatives, and move solutions from research to production using simulation and large-scale datasets.

Lead end-to-end ML and perception efforts for future sensing in autonomous vehicles: design sensor architectures, multi-modal fusion, model development, metrics, simulation-driven evaluation, system integration, and production readiness while mentoring engineers and building scalable data and training infrastructure.

Own the research-to-production pipeline: harden training/evaluation workflows, build packaging/deployment tooling, design release gates, optimize inference and serving for latency/throughput, establish benchmarking and validation, and instrument feedback from production to accelerate research iteration.

Develops and productionizes deep learning and machine learning perception models for robotics and recycling sortation. Responsibilities include experimenting with neural network architectures, designing computer vision solutions, conducting statistical experiments, deploying successful models, collaborating with data, modeling, and cloud infrastructure teams, and improving ML infrastructure.

Develop and productize deep learning perception systems for autonomous driving, including traffic signal, sign, road marking, and construction object understanding. Lead end-to-end model development, data-driven improvement, simulation, augmentation, and deployment to embedded platforms. Optimize solutions for accuracy, safety, latency, robustness, and regional generalization while collaborating with data-labeling teams and contributing technical leadership.

Builds and maintains full-stack software products and backend services with a strong Java and Spring Boot focus. Develops APIs, UI, infrastructure, CI/CD pipelines, and production machine learning or GenAI systems. Practices test-driven development, supports incident response, addresses security and compliance findings, performs root-cause analysis, and coaches junior engineers. Collaborates on architecture, participates in Agile ceremonies, and improves observability, deployment readiness, and product quality.

Founding Engineer responsible for building FilmoreAI’s production AI and data platform for construction equipment dealers. The role covers agent infrastructure, model routing, LLM extraction and reasoning, canonical data modeling, entity resolution, Python data pipelines, Azure cloud orchestration, evaluation, observability, and safe human-approved write-back. The engineer will work independently in an ambiguous startup environment and use agentic development tools to ship reliable systems with strong cost, latency, accuracy, and operational guardrails.

Build and deploy scalable AI and machine learning products for clinical research, including patient recruitment, enrollment forecasting, and study feasibility. Develop ML models, LLM-powered agents, data pipelines, and MLOps infrastructure using cloud services and CI/CD automation. Collaborate with cross-functional teams to integrate AI into clinical research workflows, monitor model performance, and advance reliable, unbiased solutions that improve patient outcomes.

Designs, deploys, and supports scalable AI/ML solutions, including predictive models, large language model applications, agentic workflows, and RAG pipelines. Builds production-grade MLOps and DevOps practices covering CI/CD, versioning, testing, monitoring, retraining, and rollback. Manages cloud deployments, observability, security, governance, HIPAA-aligned data practices, API integrations, documentation, and troubleshooting across data, infrastructure, model, and orchestration layers.

Design and deploy production recommendation and retrieval engines, build data orchestration pipelines for multiple modalities, implement agentic workflow frameworks, and prototype, optimize, and scale models with monitoring, governance, and low-latency SLAs.

Build and operate production machine learning training systems on Amazon SageMaker using AWS Trainium. The role focuses on optimizing PyTorch code for Trainium hardware, understanding NeuronCore compilation, diagnosing device-level issues, tuning distributed training for cost and throughput, and delivering production pipelines. The engineer will collaborate directly with client and internal teams in a forward-deployed engineering environment.

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.

Own and improve Quora’s second-phase ranking models for feed and digest experiences. Build end-to-end machine learning systems covering data pipelines, candidate extraction, feature engineering, model training, and production integration. Identify new product applications for machine learning, develop efficient algorithms with other ML engineers, and contribute methodologies for large-scale recommendation systems.

Own the production runtime for Phare’s machine learning stack by deploying, serving, and scaling models across GPU inference endpoints and batch or streaming workflows. Build progressive delivery pipelines, automated rollbacks, observability, alerting, and incident response processes. Manage model registries, retraining lifecycles, versioning, data lineage, security controls, and reproducible releases using Kubernetes, Terraform, Docker, and CI/CD.

Build and operate production machine learning systems for Safety AI, including low-latency APIs, data pipelines, model serving, evaluation, monitoring, and rollout infrastructure. Process large-scale camera and telematics data, optimize cloud and edge-to-cloud execution, track model performance and drift, and partner with applied scientists, firmware engineers, platform teams, and product managers to deliver reliable safety features.

Build and deploy production generative AI and machine learning systems for utility clients. Own engagements end to end, including discovery, architecture, retrieval, model selection, evaluation, deployment, monitoring, operator-facing applications, and post-go-live support. Integrate with client data and business systems, establish AI safety and governance controls, manage scope and risk, and communicate with technical teams and executives. Contribute reusable accelerators, evaluation frameworks, and reference architectures across client engagements.

Build and deploy AI applications, agents, intelligent workflows, document-processing solutions, predictive analytics, and automation. The role spans AI use-case assessment, solution design, prototyping, evaluation, testing, production deployment, monitoring, governance, and responsible-AI controls. The engineer will integrate AI with enterprise systems, APIs, workflows, and data platforms while partnering with engineering, cybersecurity, governance, and mission teams in a federal environment.