Top Machine Learning Jobs

Build, deploy, scale, monitor, and maintain production machine learning models and infrastructure. The role involves developing ML components, optimizing algorithms and data pipelines, operating distributed systems and cloud-based platforms, automating testing and deployment, and applying responsible AI and model governance practices. Candidates collaborate with product, data science, and Agile engineering teams while using technologies such as Python, PyTorch, Spark, Kubernetes, and major cloud platforms.

Build, deploy, scale, and operate production machine learning models and platforms for risk management. Responsibilities include developing ML applications, distributed training and serving systems, cloud architectures, data pipelines, model monitoring, retraining, testing, CI/CD, governance, and responsible AI practices. The role collaborates with Product, Data Science, GRC, and cross-functional Agile teams while solving complex business problems with advanced algorithms and open-source frameworks.

Leads hands-on architecture, development, and production deployment of scalable AI, machine learning, and LLM solutions for asset and wealth management. Designs data-processing pipelines, model inference services, and cloud-based platforms; experiments with and productionizes ML models; and partners with technical, product, and business teams. The role also requires leadership in AI innovation, solution design, data management, scalability, security, and communication with senior stakeholders.

Build and deploy production machine learning systems, including LLMs, agents, and RAG applications, for healthcare, government, energy, and other regulated industries. Responsibilities include developing scalable data pipelines, optimizing model performance, monitoring deployed models, translating research into practical solutions, collaborating with leaders and cross-functional teams, and contributing to software engineering practices. The role is designed for a 2027 bachelor's graduate with hands-on generative AI and machine learning framework experience.

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.

Build and own Clera’s AI/ML intelligence layer, including recommendation systems, ranking algorithms, and LLM-powered candidate-role matching. Develop production-ready features, data pipelines, and services while collaborating cross-functionally and shipping frequently. The role requires hands-on experience with the listed development tools, TypeScript, React, startup environments, and a willingness to work six days per week in a San Francisco hacker house.

Develop and deploy machine learning models for fraud detection and credit risk decisioning. Own the full ML lifecycle, including data exploration, feature engineering, experimentation, validation, deployment, monitoring, and optimization. Analyze fraud trends and portfolio outcomes, collaborate with Product, Engineering, and Risk Operations, apply generative AI tools, and mentor fellow scientists while delivering measurable improvements in fraud prevention and responsible credit access.

Architects, develops, deploys, and operates large-scale machine learning systems that optimize merchant delivery zones and marketplace performance. Responsibilities include model training, feature engineering, ML infrastructure, real-time inference, experimentation, monitoring, and automated retraining. The role partners with Product, Data Science, Operations, and Infrastructure teams while leading technical initiatives and mentoring engineers.

Develop and deploy computer vision machine learning models for customer applications. Responsibilities include data preparation, model training and fine-tuning, computer vision algorithm development, model optimization for edge hardware, evaluation, production monitoring, drift diagnosis, and retraining. The role involves collaborating with customers, product teams, and solutions engineers, supporting on-site deployments, and contributing to shared ML tooling.

Design, develop, test, and deploy AI/ML services and agentic workflows for rugged tactical networking and computing systems. Translate business needs into technical requirements, collaborate with technical owners, and build scalable applications using Python, Linux, cloud-native infrastructure, AI frameworks, databases, observability tools, and CI/CD pipelines. The role supports mission-critical government, defense, and intelligence applications.

Design and implement production agentic AI systems, RAG pipelines, intelligent document processing services, embedding and vector retrieval workflows, and multi-agent extraction systems. Build FastAPI and Java/Spring Boot services, databases, evaluation harnesses, and governance controls. Own major AI platform components, collaborate with architecture and business teams, and mentor mid-level engineers while driving technical excellence and production quality.

Build and own the recruiting marketplace’s AI/ML intelligence layer, including recommendation, ranking, retrieval, and LLM-powered candidate-role matching. Ship production features rapidly, architect data pipelines and model services, and translate founder product goals into deployed systems. The role requires applied machine learning, recommender systems, production LLM API experience, and strong Python or TypeScript skills. Work is fully on-site in San Francisco six days per week.

Designs scalable backend services, distributed systems, event-driven data pipelines, and production machine learning models. Responsibilities span feature engineering, model training, deployment, monitoring, real-time decision engines, microservices, cloud platforms, and AI/LLM workflow orchestration. The role requires collaboration across engineering, data, and product teams and is performed on-site in Manchester, New Hampshire, with no relocation available.

Leads the architecture and technical execution of Cisco’s distributed AI platform. Designs high-concurrency Go and Python microservices, agentic AI orchestration, identity and multi-tenant systems, API gateways, and secure service communication. Authors technical specifications, drives zero-trust security, CVE remediation, production triage, and platform reliability. Mentors senior engineers and establishes engineering standards across teams.

Lead design and delivery of end-to-end ML solutions for Wealth Management, build and deploy scalable models (recommender systems, GNNs, LLMs), run large experiments, validate and monitor production models, partner with cross-functional teams, and mentor data scientists to drive measurable business outcomes.

Develop and evaluate machine learning and AI solutions for Ricoh’s multilingual translation platform. Projects may include machine translation quality evaluation, terminology checking, delta translation, LLM optimization for structured content, and automated translation pipelines. The intern will contribute production-oriented code, design experiments, analyze AI outputs, collaborate across engineering and documentation teams, and present findings to technical stakeholders.

Build and evaluate AI-powered machine translation systems for multilingual technical documentation. Develop terminology checking, delta translation, LLM workflows, document parsing, and automated translation pipelines. Design experiments, analyze model outputs, contribute production code, collaborate with engineering and localization teams, and present findings to technical stakeholders.

Designs, builds, and operates production-grade agentic AI systems at enterprise scale. Responsibilities include multi-agent orchestration, tool use, planning, memory, retrieval-augmented generation, hybrid search, model integration, evaluation, observability, safety guardrails, and governance. The role provides architectural leadership, mentors engineers, leads design reviews, and develops scalable AI infrastructure across hyperscalers and company platforms.

Supports machine learning development for fraud detection, including data preprocessing, feature engineering, model training, evaluation, experiment tracking, drift monitoring, and batch scoring. Maintains Python data pipelines, Jupyter reporting artifacts, and MLflow documentation. Assists with NLP/NER transformations and UiPath Maestro integration testing while following government security, data governance, CI/CD, and EPLC documentation requirements.

Design and scale reinforcement learning post-training and distillation algorithms for simulation and autonomous driving. Develop reward models, realism critics, and discriminators for closed-loop multi-agent simulation. Conduct large-scale TPU ablation studies using driving behavior metrics to address simulator exploitation and distribution shift. Implement tested JAX code and collaborate with simulation, world modeling, and autonomous driving researchers and engineers.

Early-career engineer responsible for characterizing AI/ML training and inference workloads, analyzing compute and memory bottlenecks, and building performance models for hardware and software systems. The role partners with architecture, compiler, runtime, and product teams on optimization decisions, benchmarking, and reproducible technical analyses. Candidates apply mathematical reasoning, computer architecture knowledge, Python-based data analysis, and machine learning frameworks. Travel is required up to 10%.

Lead end-to-end machine learning solutions for League of Legends, from problem framing to production. Build personalization, recommender, matchmaking, and in-game ML systems; design evaluations and experiments; integrate models with game and service engineering; create reusable ML patterns; mentor engineers and improve ML observability, monitoring, and launch practices.

Lead the design, development, optimization, and production deployment of advanced machine learning models and algorithms. Build scalable ML pipelines, preprocess and analyze large datasets, ensure data quality, monitor model performance, and collaborate with data scientists, software engineers, and product teams to integrate AI/ML solutions into products and services.

Build and operate distributed GPU training platforms for foundation models. Run and debug multi-node PyTorch and Ray Train workloads, tune GPU networking and scheduling, profile performance, and develop benchmarks and evaluation harnesses. Partner with infrastructure teams and client scientists to improve throughput, reliability, cost efficiency, observability, and end-to-end training pipelines on GKE.

Lead BigHat’s machine learning strategy and a senior ML team developing antibody property models, generative protein models, and agent services. Build production-grade ML and MLOps capabilities, integrate models with automated wet-lab data, deliver computational insights for therapeutic programs, and develop platform offerings for AI and biopharma partners. The role combines technical leadership, hands-on modeling, team mentorship, external engagement, and accountability for reproducible, benchmarked ML impact on antibody design.