Top Machine Learning Jobs

Own end-to-end machine learning model quality for autonomous robots, including dataset curation, training, evaluation, and production deployment. Work with multimodal sensor data from cameras, IMUs, LiDAR, and odometry to develop perception, planning, navigation, and manipulation models. Build low-latency, continuous inference systems and collaborate with teleoperations and fleet operations teams. Experience with large-scale multimodal models, distributed GPU training, production ML code, and cluster management is valuable.

Build, evaluate, integrate, and operationalize machine learning, deep learning, and generative AI capabilities for federal mission applications. Responsibilities include developing models and datasets, creating AI services and interfaces, integrating APIs and pipelines, deploying scalable and reproducible solutions, documenting workflows, and improving capabilities through technical and user feedback. The role collaborates with software and data engineers, technical leaders, subject-matter experts, and mission users in a controlled government environment.

Design and guide enterprise-scale, cloud-native solutions leveraging software engineering and AI. Provide technical leadership across SDLC, design mobile/cloud apps, integrate via APIs and data services, implement CI/CD and release/configuration practices, and architect end-to-end ML pipelines for cloud and edge deployment while aligning solutions with business objectives.

Own end-to-end machine learning systems for email threat detection, from data strategy and modeling through production deployment, observability, capacity planning, and rollout. Set architecture and technical direction for model serving, retrieval, GPU workloads, and LLM-based systems. Benchmark alternatives, resolve production incidents, establish reproducible ML standards, mentor engineers, and communicate technical recommendations to product and executive leadership.

Researches, designs, and validates machine learning, sensor-fusion, tracking, estimation, and probabilistic models for real-time airspace awareness. Develops algorithms such as Kalman filters, particle filters, Bayesian filters, and multi-hypothesis tracking; conducts simulations, benchmarks performance, analyzes multimodal datasets, quantifies uncertainty, and partners with software engineers to deploy production-ready systems.

Lead development of complex data pipelines, AI/ML systems, and software solutions. Provide technical leadership, mentor engineers, conduct code reviews, make architectural decisions, and collaborate with technical and non-technical stakeholders. Maintain hands-on Python development using scalable data engineering practices, cloud platforms, containerization, CI/CD, and machine learning frameworks. Experience with large language models and AI agents is highly desirable.

Build and own production-grade machine learning systems spanning data, training, evaluation, deployment, and observability. Translate research prototypes into reliable infrastructure, establish engineering standards, maintain experimentation pipelines, and partner closely with researchers. The role requires strong distributed systems experience, Python expertise, familiarity with PyTorch or JAX, and experience with tools such as Ray, Slurm, or Kubernetes.

Build and operate production machine learning training systems on Amazon SageMaker using AWS Trainium. The role focuses on optimizing PyTorch code for Trainium hardware, understanding NeuronCore compilation, diagnosing device-level issues, tuning distributed training for cost and throughput, and delivering production pipelines. The engineer will collaborate directly with client and internal teams in a forward-deployed engineering environment.

Build and integrate machine learning systems using real-world sensor, document, and operational data. Responsibilities include developing Python data pipelines, training and evaluating models, integrating models or LLM workflows into tools, investigating failure modes, documenting results, and using version control, testing, code review, and experiment tracking. The intern will own a scoped project and present findings and recommendations at the end of the summer.

Explore, prototype, and productionize AI solutions for clinical data review, focusing on LLMs, agentic AI, RAG, and intelligent workflows. Build modular agent systems, APIs, and interfaces; integrate clinical data sources; evaluate and monitor AI performance; and ensure scalable, reproducible, traceable, auditable solutions aligned with regulatory expectations. Collaborate with clinical experts, product managers, and engineering teams while contributing to AI innovation strategy.

Own and develop a production recommendation engine for Kubernetes resource optimization. Design time-series forecasting models, data-quality pipelines, safety guardrails, regression tests, and production metrics for CPU, memory, GPU, and JVM workloads. Investigate customer issues, improve recommendation accuracy, maintain Python services, and guide ML strategy while collaborating with platform engineering, product, and leadership.

Lead end-to-end ML systems for identity verification, including document authenticity, face match, liveness and deepfake detection. Build GNN-based identity graphs, behavioral/device-intelligence models, vendor benchmarking/evaluation, and production enforcement. Mentor engineers and align ML architecture with Product, Compliance, Risk, and Security stakeholders.

As an Applied Scientist, you'll develop and deploy ML models, enhance pricing systems, and work with structured and unstructured data to improve decision-making.

The Applied Scientist will tackle various machine learning problems, enhance valuation systems, and deploy deep learning models while collaborating with cross-functional teams.

Lead research and production deployment of AI systems for intelligent code generation. Define the roadmap for agentic GenAI, DSL-aware code synthesis, model planning, evaluation, and tool integration. Improve distributed training and inference efficiency while partnering with engineering and product teams to deliver reliable customer capabilities. Mentor research teams and shape long-term AI strategy, architecture, investments, and organizational direction.

Develop and deploy state-of-the-art physical AI for outdoor robotics, focusing on perception, planning, control, simulation-driven training, multi-sensor integration, data pipelines, and GPU-optimized training and inference.

The Senior AI Scientist will develop innovative machine learning techniques for MRI, coordinating with teams and enhancing product capabilities through advanced model development and software maintenance.

Develop and optimize lightweight machine learning models for edge AI applications, integrating into production systems and mentoring junior engineers.

The Machine Learning Engineer will develop and improve ML models for product surfaces, build data processing pipelines, and leverage machine learning technologies in advertisements.

Design, implement, and deploy machine learning algorithms and scalable ML solutions; collaborate with data scientists, engineers, and federal customers to build analytics capabilities and features; support application deployment and customer enablement.

As a Machine Learning Engineer Intern, you'll develop neural decoders for Brain-Computer Interfaces and collaborate with cross-functional teams on new functionalities.

As an Applied Scientist II, you will work with cross-functional teams on projects that optimize software systems and improve ML components, while analyzing inefficiencies and proposing solutions.

The Software Intern at Cadence will work on research and development using machine learning technologies for on-chip parasitic modeling in a team environment.

Lead the architecture, development, modernization, performance optimization, and reliability of enterprise-scale machine learning platform software. Mentor engineers, influence cross-team technical decisions, conduct code reviews, evaluate AI-assisted code, design cloud-native systems, and drive high-quality delivery across complex application layers. The role requires expertise in distributed systems, cloud infrastructure, production services, and enterprise engineering practices.

Develop and implement machine learning and optimization algorithms to improve business KPIs. Analyze large datasets, produce production-quality software components, design experiments, and collaborate with teams to improve data collection, modeling, and scalable systems.