Top Machine Learning Jobs

Develop and improve Jane Street’s ML compiler stack for high-throughput training and low-latency inference. Responsibilities may span compiler optimization, graph compilation, GPU kernel programming, frontend design, ML training and inference systems, debugging, formal methods, and programming-language or DSL development. The role involves evaluating existing technologies and building custom solutions to improve hardware utilization and accelerate model development and deployment.

Own fraud prevention machine learning end to end, including deploying anomaly detection and device risk models, building labeled datasets and evaluation methods, designing online and offline features, modernizing training and serving infrastructure, investigating fraud patterns, and monitoring production systems. The role partners with data engineering, backend, and product teams and participates in on-call operations.

Develops, trains, deploys, and maintains deep learning models and ML pipelines for electro-optical and infrared image processing. Responsibilities include data ingestion, feature engineering, model validation, inference, rapid prototyping, troubleshooting, system maintenance, and evaluating emerging ML tools. The role collaborates with AI, machine learning, and computer vision engineers supporting space-based defense and intelligence applications.

Build and operate production machine learning systems for EV valuation, auction pricing, forecasting, demand prediction, battery health, and document extraction. Responsibilities include feature pipelines, model training, evaluation, deployment, monitoring, drift detection, retraining, experimentation, and LLM evaluation. The role also strengthens ML engineering practices through reproducible training, model registries, documentation, code review, and collaboration within a small, high-autonomy team.

Develop and deploy machine learning and deep learning models for battery, materials, and energy-system applications. Responsibilities include designing novel architectures, prototyping Transformers and LLMs, conducting experiments and benchmarking, incorporating scientific constraints, documenting results, presenting findings, and tracking advances in ML research. The role collaborates with battery scientists and interdisciplinary R&D teams in a fully on-site environment.

Design, train, test, deploy, monitor, and maintain ML models (including LLMs/generative AI) for national security missions. Collaborate with data engineers, software developers, data scientists, and stakeholders to apply ML algorithms, triage large datasets, and deliver production-safe, mission-critical AI/ML solutions.

Build and deploy production machine learning systems for quoting, material selection, planning, and factory decisions. Responsibilities include developing datasets, features, models, evaluation systems, and pipelines; monitoring quality, drift, latency, and business impact; designing safeguards and fallbacks; improving data quality and explainability; and collaborating with software engineers, operators, and domain experts.

Train and evaluate machine learning models that improve large language model inference efficiency and capability. Responsibilities include speculative decoding, quantization, sparsity, distillation, pruning, parameter-efficient fine-tuning, agentic systems, benchmarking, research implementation, and reinforcement learning or post-training. The role collaborates with performance, compiler, runtime, and product teams to ship model improvements.

Supports the full machine learning lifecycle, including data cleaning, preprocessing, feature engineering, model development, experimentation, evaluation, optimization, deployment, and production monitoring. Collaborates with senior engineering and technology leaders to translate business requirements into scalable ML solutions and improve predictive analytics systems.

As a Senior Scientist, you will develop machine learning methods for biologics design, optimize large-molecule therapeutics, and work collaboratively with experimental teams to enhance drug development processes.

Develop, train, evaluate, and operationalize machine learning models for production applications. Responsibilities include dataset preparation, feature engineering, pipeline development, model experimentation, validation, deployment, model serving, performance monitoring, drift investigation, retraining recommendations, documentation, and collaboration with data science and software teams.

Leads AI/ML infrastructure networking initiatives by benchmarking switches, validating ASIC and fabric features, and guiding workload deployments. Develops AI-driven network automation using Python, Ansible, SONiC, Junos, and related tools. Tests protocols, congestion behavior, traffic distribution, resiliency, and high-performance network operations. Documents architectures, methodologies, configurations, results, and recommendations for scalable AI/ML network fabrics.

Own and optimize deep learning training performance across GPUs, cloud infrastructure, and distributed environments. Profile workloads, develop custom GPU kernels, tune PyTorch and training systems, improve memory and compute efficiency, and design scalable distributed training strategies. Collaborate with researchers and infrastructure teams to preserve numerical correctness while improving speed, cost, reliability, and scalability. Build benchmarking, checkpointing, recovery, and regression-testing workflows for long-running AI/ML experiments.

Develop and deploy state-of-the-art machine learning methods for multimodal human health data, including imaging, EHRs, biomarkers, longitudinal outcomes, genetics, and omics. Build precise phenotypes, model patient states, predict clinical endpoints, identify therapeutic targets, and develop biomarkers. Create reusable production ML components while collaborating with machine learning scientists, statistical geneticists, life scientists, software engineers, and clinicians.

Build and deploy production machine learning systems across experimentation, architecture, MLOps, and infrastructure. Develop continual-learning pipelines, agentic ML systems, observability, explainability, monitoring, retraining, and drift detection. Manage Google Cloud-based ML infrastructure and high-volume production applications while collaborating with technical and external stakeholders. Work with geospatial and Earth observation data, iterative Agile development, and systems that evolve from prototypes into customer-facing products.

Develop and deploy reliable machine learning, LLM-powered agent, and agentic workflow systems for government and enterprise customers. Integrate models with tools, APIs, MCP servers, and data sources; build evaluation frameworks for performance, reliability, and safety; improve Chariot, the company’s AI operations platform; and support mission-critical deployments. Collaborate with data scientists, software engineers, DevOps teams, and customers in secure government environments.

Develop and test neural-network software for Department of Defense systems using Python and C++. Responsibilities include researching architectures, curating datasets, building training and evaluation pipelines, optimizing model performance, conducting reproducible experiments, debugging software, and documenting technical results.

Design, build, deploy, and operate production LLM-powered agents for JPMorganChase’s Commercial and Investment Bank. Responsibilities include Graph RAG, agent memory, context and authorization management, multi-agent workflows, MCP integrations, evaluations, guardrails, cloud deployment, observability, resiliency, and security. The role partners with business, product, engineering, and external stakeholders to deliver supported AI solutions.

Leads AI/ML engineering teams and technical strategy for enterprise-scale healthcare and pharmacy solutions. Oversees development, deployment, governance, MLOps, security, and operations for generative AI, LLM, NLP, predictive analytics, and agentic AI capabilities. Partners with product, engineering, data science, and executives to align technology with business goals, evaluate emerging technologies, improve automation, and develop engineering talent.

Build and operate large-scale machine learning systems for ads personalization, including ad ranking, conversion-rate prediction, recommendation, behavioral modeling, and bidding optimization. Monitor model quality, calibrate predictions, address data drift and delayed feedback, and guide technical direction and project execution. The role requires collaboration with engineering leads and communication of complex technical concepts to non-engineering stakeholders.

Optimize multimodal foundation-model inference across AMD GPUs, TPUs, embedded Jetson hardware, consumer GPUs, and specialized accelerators. Responsibilities include kernel tuning, compilation, quantization, pruning, distillation, model parallelism, benchmarking, regression detection, numerical validation, and hardware cost-performance analysis. The role also supports real-time on-robot inference, hardware selection, and collaboration with research, robotics, and infrastructure teams.

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.

Build and deploy scalable AI/ML systems for retrieval, ranking, categorization, triage, and generative AI across healthcare data. Own solutions end-to-end from problem framing and model selection through production deployment and optimization. Collaborate with software engineers, product managers, clinicians, and operations teams to translate complex business problems into explainable, maintainable systems with measurable outcomes.

Lead AI/ML engineering strategy, roadmap, team development, hiring, and technical execution. Oversee production machine learning systems spanning search, recommendations, pricing, forecasting, fraud, and counterfeit detection. Guide model architecture, experimentation, generative AI adoption, ML platforms, and MLOps while partnering with Product, Data Science, and business leaders to deliver measurable marketplace outcomes.

The Applied Scientist will develop and deploy machine learning products for content understanding, creator sponsorship, advertising, and brand safety. Responsibilities include selecting and evaluating ML approaches, working with behavioral, metadata, text, and video data, prototyping models, deploying them through data pipelines and ML services, and collaborating with scientists, engineers, product teams, and business stakeholders. The role also contributes to innovation in LLMs, computer vision, inference, and model evaluation.