Top Remote Machine Learning Engineer Jobs

Build and improve AI recruiting agents using modern LLMs, Python backend services, APIs, prompts, RAG, workflows, and agent orchestration. Evaluate model performance, experiment with AI technologies, collaborate with product and engineering teams, and deploy scalable production systems. The role requires Python, LLM and AI API experience, familiarity with agent frameworks and FastAPI, and cloud platform knowledge. Production AI product experience, model evaluation, tool calling, prompt engineering, and open-source contributions are preferred.

Design and optimize large-scale machine learning model-serving systems, including distributed infrastructure, load balancing, auto-scaling, batching, caching, and GPU inference optimization. Build reliable, high-concurrency services handling billions of requests, benchmark and accelerate inference engines, develop CI/CD for model deployment, and partner with ML engineers to fine-tune and deploy open-source large language models.

Lead the design, development, testing, deployment, and productization of scalable AI/ML solutions. Build and optimize machine learning and deep learning models, transform prototypes into secure enterprise systems, and develop production-grade MLOps pipelines and CI/CD workflows. Guide AI/ML strategy, roadmaps, cloud deployments, and enterprise integration while mentoring engineering teams, advising customers, supporting workforce planning, and collaborating with technical and government stakeholders.

Develop data-centric machine learning solutions for autonomous driving. Design dataset mixtures, curation and mining methods, model-training recipes, scaling studies, and offline evaluations using real and synthetic driving data. Apply self-supervised learning, imitation learning, reinforcement learning, and foundation-model fine-tuning. Train models across large multi-GPU and multi-node datasets, diagnose data-related failures, and collaborate on deploying models into onboard driving systems.

Develop production LLM-powered agents and machine learning solutions for customer risk identification and threat detection. Translate threat-research methods into agent logic, build data requirements and pipelines, evaluate precision and recall, backtest and tune models, and deploy and monitor reliable solutions using CI/CD. Collaborate with threat researchers and data engineers while addressing production reliability, observability, data quality, cost, latency, and accuracy.

Lead design, development, deployment, and maintenance of responsible AI/ML solutions (including LLMs, generative models, chatbots, and agent orchestration) for provider-facing products. Provide technical leadership, mentor engineers, align solutions with enterprise AI strategy and compliance, integrate AI into cloud architectures, and ensure production reliability and operational excellence.

Design, train, evaluate, and deploy reinforcement learning systems for complex decision-making problems. Responsibilities include developing RL algorithms, simulation environments, reward models, and neural network policies; scaling training on GPU clusters; and delivering stable, safe production systems. The role also involves research, communication, and continuous policy improvement, with preferred experience in RLHF, multi-agent or hierarchical RL, robotics, control systems, autonomous driving, and RL publications.

Design, develop, and deploy AI-powered healthcare solutions using Java and Spring Boot microservices. Implement HL7 FHIR and Da Vinci interoperability standards across prior authorization, claims, eligibility, and provider domains. Build Kafka-based event-driven services, integrate relational databases, automate CI/CD, deploy with Docker and Kubernetes on AWS or Azure, and establish monitoring using Splunk, ELK, Prometheus, and Grafana.

Build and operate production retrieval-augmented generation systems, including embeddings, vector retrieval, grounded language-model generation, citation validation, prompt and schema design, model packaging, serving, rollback, and telemetry. Develop bounded model assistance for narrative extraction with provenance and rule-context tracking. Maintain resilient serving paths in secure government cloud environments, including restricted or air-gapped deployments.

Lead end-to-end LLM inference performance across serving systems, models, GPU hardware, and workloads. Define rigorous benchmarks, diagnose bottlenecks from scheduling through kernels and interconnects, optimize single- and multi-node deployments, and deliver production-ready runtimes and configurations. Collaborate with product and infrastructure teams, evaluate inference technologies, and implement runtime fixes. The role requires strong Python engineering, production experience with serving engines such as vLLM or SGLang, GPU profiling, and knowledge of modern inference optimization techniques.

Leads an engineering team building scalable ML data infrastructure, including data ingestion, processing, storage, access, pipelines, and analytical platforms. Defines architecture, evaluates technologies, improves performance and reliability, translates data requirements into technical solutions, and ensures governance, security, and regulatory compliance. Provides mentorship, strategic roadmap input, and cross-functional collaboration with ML engineers, data scientists, platform engineers, and product teams.

Develop and deploy machine learning models for autonomous truck behavior systems using imitation learning, reinforcement learning, and sequence modeling. Build production ML code, training pipelines, data workflows, evaluation tooling, and inference systems. Analyze model performance and failure modes, collaborate with autonomy and simulation teams, and integrate learned behavior models into simulation and validation workflows.

Leads the design, development, deployment, and operation of scalable machine learning solutions and high-performance microservices. Builds real-time inference systems using Triton and TensorRT, guides distributed cloud architectures, participates hands-on in coding and reviews, and mentors engineers. Drives technical strategy, operational excellence, system resilience, and business-wide innovation across generative AI, graph machine learning, and big data initiatives.

Build and deploy spatial AI capabilities for generative home floor plans. Responsibilities include training and adapting ML models, creating APIs and evaluation pipelines, improving reproducibility, managing latency and cost, extending research into product features, partnering on MLOps and deployment, and mentoring engineers and researchers.

Lead production-grade ML systems: build data pipelines, training and evaluation workflows, and scalable GPU inference. Fine-tune and adapt large models (LoRA, QLoRA, SFT, DPO, distillation), optimize deployments for latency/cost/reliability, and collaborate with product and engineering to ship robust, observable model-driven features.

Design, build, and operate large-scale ML systems across data, training, evaluation, inference, and deployment. Lead GPU-optimized training and inference pipelines, ensure reliability, performance, safety, and seamless integration into products. Own production deployments, monitoring, and cross-functional best practices to drive measurable model improvements and efficient research-to-production cycles.

Build and maintain core infrastructure for developing, training, evaluating, deploying, and operating machine learning models and pipelines for Atlassian’s GenAI products. Solve complex software infrastructure and architecture challenges, lead engineers through technical projects from design to launch, and collaborate with internal teams and customers. The role involves large-scale distributed systems, data processing, cloud platforms, CI/CD, and machine learning lifecycle support.

Design and optimize large-scale machine learning model-serving systems, including distributed infrastructure, load balancing, auto-scaling, batching, caching, and inference optimization. Build highly reliable, high-concurrency services serving billions of requests, improve latency and throughput, benchmark inference engines, and develop CI/CD infrastructure for model deployments. Partner with ML engineers to fine-tune and deploy open-source large language models.

Leads the design, development, and deployment of AI-powered healthcare solutions using Java, Spring Boot microservices, HL7 FHIR, Da Vinci guides, Kafka, relational databases, Docker, Kubernetes, and cloud platforms. Builds REST APIs and event-driven systems across claims, eligibility, prior authorization, and provider domains. Establishes CI/CD automation, observability, security, and responsible AI practices while evaluating emerging technologies and improving operational workflows.

Develop and improve computer vision systems for aerial image recognition, including object detection, segmentation, classification, and pattern recognition. Train, fine-tune, evaluate, and deploy machine learning models; build data pipelines; experiment with foundation and multimodal models; prototype new AI applications; and optimize model accuracy, reliability, performance, and inference efficiency. The role requires independent ownership from research through production deployment.

Develop and deploy responsible AI solutions that identify, measure, and mitigate bias across Pinterest’s large-scale machine learning applications, including generative AI, search, and recommendation systems. Collaborate with trust and safety, user modeling, content understanding, and engineering teams to adopt fairness tooling and practices. Mentor engineers, contribute to technical strategy, and bridge advanced research with product impact.

Intern will develop and deploy generative AI applications using large language models, optimize neural networks for NLP and machine perception, and train or fine-tune models for scalable, reliable deployment. The role involves model building, performance optimization, cloud-native deployment, data pipelines, and collaboration within Cisco’s product organization.

Machine Learning Engineer interns develop generative AI applications using GPT-4, Claude, Llama, and transformer models. Responsibilities include building, training, fine-tuning, and deploying neural networks for NLP and machine perception, while optimizing performance, scalability, and reliability. Interns may also work with cloud-native platforms, Docker, data pipelines, AI security, validation techniques, and data science libraries.

Lead the design and delivery of scalable machine learning systems and customer-facing generative AI applications for Rovo Chat. Build agent harnesses that improve quality, reliability, and latency; advance state-of-the-art AI capabilities; lead engineers through technical design and launch; and collaborate cross-functionally with teams and internal customers. The role also involves applying a deep understanding of the machine learning project lifecycle and supporting platform-based product development.

Lead the design, development, deployment, and operation of production AI and ML systems. Build high-throughput Python services and Databricks pipelines, support traditional ML and LLM applications, establish testing and deployment standards, and develop monitoring for quality, reliability, latency, throughput, and cost. Partner with product, research, and responsible AI teams on governance and release criteria. Provide architectural leadership, conduct code reviews, mentor engineers, and resolve complex technical dependencies.