Top Machine Learning Jobs

Lead the architecture and development of Vumedi’s AI-powered discovery platform. Build recommendation systems, semantic search, retrieval and ranking pipelines, knowledge graph RAG systems, and LLM-powered experiences for physicians. Design scalable inference infrastructure, evaluate emerging AI technologies, define long-term AI architecture, and collaborate with product, data science, and engineering teams while contributing to production code.

Analyzes and reconciles data across systems, builds and maintains data pipelines, develops predictive and machine-learning models, and creates automated AI workflows for security intelligence, anomaly detection, and reporting. The role also develops LLM prompts, evaluates model quality, supports cloud and containerized infrastructure, contributes to CI/CD automation, monitors controls, creates SOPs, and provides analytical guidance to clients and management.

Leads architecture, development, deployment, scalability, reliability, and operational excellence for enterprise AI and machine learning platforms. Transforms AI research prototypes into production solutions involving LLMs, agentic systems, responsible AI, and advanced inference. Establishes MLOps, observability, governance, incident response, and reliability practices while mentoring engineers and serving as a senior technical escalation point for complex production challenges.

Build, deploy, and maintain production machine learning models and end-to-end ML pipelines. Develop MLOps frameworks with model versioning, CI/CD, registries, feature stores, monitoring, and automated retraining. Deploy solutions using cloud and containerized technologies, troubleshoot performance issues, address model drift, and collaborate with engineering and data teams on scalable mission-critical applications.

Own the architecture and long-term evolution of hybrid, multi-tenant AI compute platforms across bare-metal OpenShift and public-cloud services. Define distributed training networking, GPU utilization and cost models, GitOps governance, model-serving standards, workload placement, identity and security controls, SLOs, disaster recovery, and platform upgrade strategies. Partner with AI, privacy, and security teams to deliver HIPAA-compliant infrastructure for training and inference workloads.

Own the architecture and engineering of production AI/ML infrastructure powering Relay. Build model-serving, inference, evaluation, orchestration, deployment, observability, and lifecycle systems. Optimize model performance, latency, throughput, scalability, reliability, and cost while establishing feedback loops for continuous quality improvement. Partner with engineering and product teams to advance agentic AI capabilities and make foundational architectural decisions in a rapidly evolving environment.

Build and deploy production machine learning applications, APIs, inference services, and data platforms. Engineer large-scale pipelines using distributed technologies, data modeling, ETL/ELT, transformation, and data quality practices. Translate defense and mission needs into scalable solutions across cloud, containerized, and GPU/CPU environments while collaborating with engineering, data, and mission teams.

Leads the end-to-end design, deployment, and performance of real-time ML prediction and bid-optimization systems for programmatic advertising. Provides hands-on technical leadership to applied scientists and ML engineers, formulates optimization problems, manages production ML challenges such as latency, delayed feedback, drift, and retraining, and aligns technical roadmaps with business priorities.

Improve the performance and efficiency of machine learning training and serving systems. Develop CUDA and Triton GPU kernels, optimize hardware and distributed system performance, design training and serving engines, improve GPU-to-machine communication, and build benchmarking and regression tools. The role requires hands-on NVIDIA GPU kernel development, machine learning infrastructure experience, and proven optimization work across hardware, runtimes, and multi-machine deployments.

Build and deploy production machine learning models, scalable ML infrastructure, real-time training and inference pipelines, multimodal AI systems, and model APIs. Optimize model performance for cost, speed, and accuracy while applying MLOps practices. Collaborate with data science teams to productionize research models and independently architect highly available ML systems for a global media and entertainment platform.

Designs, develops, tests, deploys, monitors, and continuously improves machine learning models for complex business and national security mission requirements. Integrates data pipelines, evaluates model performance, applies statistical analysis, ensures data and output quality, and transforms prototypes into scalable production solutions. Provides system integration oversight and may lead distributed teams. Requires advanced education or equivalent experience and an active TS/SCI clearance with polygraph.

Design, build, evaluate, and deploy LLM-powered agents and machine learning systems that improve Quora content quality and safety. The role involves developing agentic workflows, conducting offline and online evaluations, monitoring production performance, investigating failures, and improving accuracy, reliability, latency, and cost. New graduates will collaborate with experienced engineers and own projects from development through production.

Set technical direction for production AI agents, LLM and vision-language model post-training, evaluation, MLOps, and data improvement systems. Architect agent orchestration, retrieval, observability, and failure recovery; establish quality and release standards; and ship measurable model improvements. This hands-on individual contributor will co-own the ML roadmap, lead technical reviews, mentor engineers, support hiring, and implement privacy-conscious, HIPAA-aware practices.

Leads architecture, development, optimization, and production deployment of machine learning and deep learning models for edge AI systems. Develops computer vision, signal processing, sensor fusion, and distributed ML infrastructure capabilities. Optimizes models for resource-constrained AI chips and edge devices, using Python, C++, PyTorch, and TensorFlow. Provides technical leadership and collaborates with scientists, engineers, and product managers across public safety, enterprise, and defense applications.

Leads enterprise AI architecture for generative AI, agentic systems, traditional machine learning, and computer vision. Designs secure, scalable platforms; establishes MLOps and LLMOps standards; prototypes and writes production code; leads client solutioning and pre-sales; ensures regulatory compliance; mentors architects and engineers; and governs technical standards across strategic engagements.

Leads the technical direction and end-to-end delivery of machine learning and LLM systems for merchant quality, integrity, relevance, and shopping discovery. Responsibilities include architecture, implementation, evaluation, experimentation, monitoring, safety, scalability, and production rollout. The role establishes ML engineering standards, partners cross-functionally, mentors engineers, influences technical roadmaps, and supports future hiring while improving user trust and shopping outcomes.

Develop deep learning architectures and end-to-end machine learning pipelines for intelligence and national security applications. Responsibilities include sourcing and curating data, training, evaluating, testing, and deploying computer vision models in AWS using Python and PyTorch. The role also involves producing high-quality software, researching academic literature, collaborating on innovative solutions, and presenting technical work.

Develop and operationalize machine learning systems powering AI-driven insights from structured and unstructured financial data. Build NLP, generative AI, LLM, summarization, semantic search, classification, and prediction solutions. Responsibilities include model architecture, training, deployment, monitoring, optimization, scalable data pipelines, technical reviews, mentoring, research translation, and cross-functional collaboration with engineering, product, and data teams.

Develop and deploy production-grade machine learning systems that generate insights from structured and unstructured financial data. Responsibilities include NLP, summarization, semantic search, classification, prediction, GenAI, and LLM development; model training, monitoring, optimization, and operationalization; scalable data pipeline integration; architectural contributions; and collaboration with engineering, product, and data science teams.

Develop scalable AI-enabled software integrating machine learning models, APIs, data pipelines, retrieval systems, and agentic workflows. Build backend services and cloud-native applications using Python, AWS, Docker, and Kubernetes. Operationalize machine learning capabilities, improve system performance and reliability, and support model inference and information retrieval in mission environments. Collaborate with engineers and researchers while following software development, testing, and quality practices.

Develop and deploy proprietary machine learning models for a software product, managing data preparation, experimentation, production deployment, monitoring, and refinement. Build neural networks and learned representations, improve experiment tooling, collaborate with product teams, and write reliable Python. The role requires production machine learning experience, advanced modeling skills, and familiarity with transformers, large language models, and graph-based architectures. Bonus experience includes world models, robotics, simulation, cloud ML services, backend development, SQL, CAD, and geometry computing.

Own the ML platform powering production training pipelines, model serving, deployment, orchestration, observability, and multi-region scaling. Drive major infrastructure initiatives, improve reliability and cost efficiency, lead incident response, collaborate across teams, and set the technical vision for ML operations. The role is hands-on, requiring production distributed-systems expertise, Kubernetes and cloud infrastructure knowledge, technical leadership, mentoring, and strong communication.

Own and evolve Braze’s ML platform for production-scale training, deployment, serving, observability, reliability, and cost efficiency. Lead complex infrastructure initiatives, including multi-region model serving, customer-specific model pipelines, CI/CD tooling, orchestration, and cloud identity. Set technical direction, manage incidents, collaborate across teams, improve engineering quality, mentor senior engineers and data scientists, and connect platform decisions to business outcomes.

Own and evolve Braze’s ML platform for training, deploying, serving, and monitoring models at global scale. Lead complex infrastructure initiatives, multi-region model serving, CI/CD, orchestration, reliability, cost optimization, and incident response. Set technical direction, improve production quality, collaborate across teams, mentor senior engineers and data scientists, and connect platform decisions to business outcomes.

Develop and deploy deep learning models for electro-optical and infrared image processing. Build ML pipelines for data ingestion, feature engineering, training, and inference; collaborate on computer vision solutions; prototype systems for operational integration; troubleshoot and maintain software; and evaluate emerging ML tools and techniques.