Top Machine Learning Jobs

Build and deploy scalable regulatory model applications and cloud modernization solutions using PySpark, Python, Databricks, and AWS. Design secure, resilient architectures; develop distributed data processing systems; write tested, documented production code; support reliability and observability; and collaborate with product, data, model, and platform teams. The role may also enable AI agent development through LLM workflows, orchestration, evaluation, and guardrails.

Build and support scalable, production-grade regulatory model applications and cloud modernization solutions. Design distributed data processing systems using PySpark, Python, and Databricks; implement secure and resilient AWS architectures; write tested, documented code; and contribute to architecture reviews and release readiness. Collaborate with product, data, model, and platform teams. Experience enabling AI agent development, including LLM workflows, orchestration, evaluation, and guardrails, is a plus.

Lead end-to-end applied AI/ML solutions for payments trust & safety, from problem framing and data strategy through production deployment, monitoring, and iteration. Drive technical direction, evaluation metrics, reproducibility, and compliance-ready documentation while partnering across product, engineering, data, risk, and compliance stakeholders.

Build and maintain scalable machine learning infrastructure, including distributed GPU training pipelines, model promotion workflows, monitoring integrations, and ML frameworks. Optimize training throughput, support model deployment and observability, and collaborate with product, architecture, data science, and engineering teams. The role requires strong Python, cloud, ML framework, big data, training and inference system, and enterprise design experience, with preferred expertise in recommendation systems, CUDA, containers, orchestration, and data storage.

Lead the technical direction and implementation of Current’s machine learning platform, covering feature pipelines, training data, model training, serving, monitoring, reproducibility, and production-readiness standards. Establish scalable workflows, improve training-serving consistency, support data scientists, define model governance contracts, evaluate build-versus-buy tooling, and drive adoption across engineering, data science, risk, marketing, and finance.

Develop and integrate AI-enabled features (LLMs, agents, ML) into applications, write and test production-quality code, troubleshoot performance, and collaborate with product and engineering teams. Support monitoring, evaluation, and continuous improvement of AI capabilities; may work nights/weekends as needed.

Build and ship production machine learning and computer vision systems processing 11 million daily images. Own the full lifecycle from sensor data collection, dataset curation, training, and deployment through monitoring and iteration. Develop detection, segmentation, classification, embedding, and re-identification models for cloud and roadside edge hardware, balancing accuracy, speed, and power efficiency. Apply both classical computer vision and state-of-the-art methods while writing production-quality software.

Design, build, deploy, retrain, monitor, and maintain production machine learning models and infrastructure. Develop application code, data pipelines, testing, automation, and cloud-based architectures using distributed systems and containerized environments. Collaborate with Product and Data Science teams in Agile delivery, apply responsible and explainable AI practices, optimize model performance, and support resilient deployments at scale.

Designs, develops, deploys, and supports production AI/ML solutions for healthcare operations. Builds scalable machine learning services, data pipelines, cloud applications, and MLOps workflows; implements CI/CD, monitors and optimizes deployed systems, evaluates emerging AI technologies, and collaborates with product, architecture, data science, and engineering teams. The role also mentors junior engineers and contributes to technical standards, code reviews, and architecture decisions.

Design, build, evaluate, and deploy multi-step agentic AI systems, including autonomous and multi-agent workflows. Develop orchestration frameworks, integrate LLMs with tools and APIs, and create evaluation systems measuring reliability, latency, cost, safety, and task success. Collaborate with product, engineering, and research teams to deliver production-grade solutions while mitigating prompt injection, hallucinations, unsafe tool use, and other agentic-system risks.

Build and maintain scalable data, feature engineering, inference, and machine learning pipelines. Deploy and manage end-to-end ML systems using MLOps tools, implement CI/CD and workflow orchestration, monitor model and data quality, troubleshoot production issues, and develop reusable platform components. Collaborate with data scientists, product, and business teams to deliver reliable AI/ML forecasting solutions across cloud and on-premises environments.

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 discovery, technical scoping, architecture, development, deployment, and optimization of production AI/ML software. Mentors engineering pods, evaluates emerging AI technologies, collaborates with data scientists and product teams, and ensures model and pipeline reliability, scalability, and performance. The role also promotes responsible AI, engineering best practices, automation, and technical standards.

Leads applied AI/ML science and engineering initiatives, developing machine learning models for healthcare outcomes using structured and unstructured data. Establishes reusable AI/ML platforms, production pipelines, and engineering standards; mentors scientists and engineers; collaborates with business and responsible AI teams; and promotes enterprise adoption. The role also involves technical documentation, patents, publications, model governance, and explaining complex solutions to technical and non-technical audiences.

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.

Develops search relevance solutions for an enterprise question-answering platform. Responsibilities include creating and optimizing ranking features, building frameworks for diversified ranking, enabling parallel ranking experiments, and developing metrics to evaluate search performance. The role applies information retrieval, machine learning, text processing, and natural language understanding to address lexical gaps, semantic understanding, and data sparsity while collaborating with product, UX, customer success, and engineering teams.

Design, build, deploy, monitor, and maintain production machine learning models and infrastructure. Develop optimized data pipelines, distributed systems, cloud-based architectures, and automated testing and deployment processes. Apply modeling, feature selection, validation, responsible AI, and explainability practices while collaborating with product, data science, and engineering teams in an Agile environment.

Design, build, deploy, and operate machine learning models, platforms, data pipelines, and production services at scale. Responsibilities include model development, infrastructure optimization, cloud and Kubernetes operations, automated testing, CI/CD, monitoring, retraining, governance, responsible AI, and collaboration with product, data science, and Agile engineering teams.

Build, deploy, scale, and operate production machine learning models and platforms. Responsibilities include developing ML components, distributed data pipelines, cloud and Kubernetes infrastructure, model monitoring and retraining, CI/CD automation, responsible AI governance, and collaboration with product, data science, and engineering teams.

Design, build, deploy, optimize, and monitor production machine learning models and infrastructure. Develop scalable data pipelines, distributed systems, cloud-based architectures, and automated testing and deployment processes. Collaborate with Product and Data Science teams, apply responsible AI practices, maintain model governance, and support resilient production services using modern ML frameworks, Kubernetes, and cloud platforms.

Build, deploy, scale, and operate production machine learning models and infrastructure. Responsibilities include developing ML applications, optimizing models and data pipelines, managing cloud-based distributed systems, automating testing and deployment, monitoring production models, and applying responsible AI practices. The role collaborates with Product and Data Science teams in an Agile environment and uses technologies such as Python, PyTorch, Spark, Kubernetes, and cloud platforms.

Build, deploy, scale, and maintain machine learning models and platforms in production. Responsibilities include developing ML applications, distributed data pipelines, cloud and Kubernetes infrastructure, automated testing and deployment, model monitoring, retraining, governance, and responsible AI practices. The role collaborates with Product, Data Science, and Agile engineering teams while solving complex business problems using Python, Scala, Java, and large-scale ML technologies.

Build, deploy, scale, and maintain production machine learning models and platforms. Responsibilities include designing ML systems, developing data pipelines, operating distributed systems and cloud infrastructure, automating testing and deployment, monitoring and retraining models, and applying responsible AI practices. The role collaborates with Product and Data Science teams and uses Python, Java, Scala, or related languages.

Develop autonomy evaluation metrics, statistical and machine-learning analyses, dashboards, and pipelines for simulation and on-road autonomous vehicle testing. Analyze perception, prediction, and planning performance; identify anomalous behavior and critical scenarios; apply VLMs and LLMs where appropriate; and support release gating and safety decisions. The role requires production Python, C++ debugging, technical leadership, cross-functional collaboration, and rigorous software engineering practices.

Build, deploy, scale, and maintain machine learning models and platforms in production. Develop optimized data pipelines, distributed ML systems, cloud-based architectures, and automated CI/CD workflows. Collaborate with product and data science teams, monitor and retrain models, apply responsible AI practices, and ensure secure, governed software delivery. The role requires extensive experience with ML frameworks, distributed systems, cloud services, Kubernetes, and production machine learning operations.