Top Senior Machine Learning Jobs

Lead the architecture, development, and deployment of scalable machine learning systems, including the AI/ML integration connecting Xometry with partner manufacturing platforms. Build real-time ML serving, data pipelines, MLOps, governance, monitoring, and observability. Drive technical roadmaps, optimize latency and throughput, ensure software quality and security, collaborate across engineering and product teams, and mentor engineers while delivering production AI capabilities.

Lead the architecture and delivery of scalable machine learning systems, including real-time model serving, MLOps, monitoring, governance, and automated deployment. Own the AI/ML integration connecting Xometry with partner design and manufacturing platforms. Develop cloud-based production systems, optimize model latency and throughput, establish technical standards, mentor engineers, and collaborate with engineering, product, data science, and business teams.

Technical leader responsible for major cross-functional ML and AI initiatives, including long-term technical roadmaps, scalable production systems, generative AI, computer vision, and 2D/3D modeling. The role drives business impact through revenue growth, cost reduction, efficiency improvements, and competitive differentiation. Responsibilities include architecture, MLOps, model governance, infrastructure decisions, stakeholder influence, team mentorship, and translating emerging technologies into manufacturing applications.

Design and deploy machine learning models for financial document processing, optimize large language models, collaborate with cross-functional teams, and guide junior peers in a fast-paced environment.

As a Lead Machine Learning Engineer, you'll manage AI/ML projects, optimize models, enhance ad-serving platforms, and mentor junior engineers.

Lead requirements and systems engineering for an enterprise application integrating AI capabilities. Work with customers, developers, systems engineers, and domain experts to define requirements, create technical specifications and Jira stories, validate features, evaluate AI architectures and model performance, and coordinate AI solution delivery through Agile development. The role requires hands-on technical execution, functional testing, stakeholder communication, and a current Top Secret/SCI clearance with favorable polygraph.

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.

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.

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.

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.

Design and build production LLM infrastructure, including orchestration, prompting, workflow layers, retrieval integrations, MCP tools, evaluation systems, dashboards, and observability. Improve platform reliability through caching, incident response, scalability, and model rollbacks. Establish architecture standards, conduct design reviews, mentor engineers, and communicate AI concepts to nontechnical stakeholders.

Lead the development, fine-tuning, deployment, and optimization of machine learning solutions involving LLMs, vision models, and NLP for healthcare applications. Build production-grade models and pipelines, support scalable GPU and TPU training, validate performance and compliance, collaborate with healthcare and product teams, and mentor junior engineers. The role also requires documenting technical approaches and applying MLOps and LLMOps practices in regulated environments.

Design, build, and operate production ML models, services, and platforms for pricing, risk, and decision systems. Own end-to-end model pipelines, deployment, monitoring, and iteration. Collaborate with researchers and product to productionize prototypes, tackle data drift and sparsity, lead design reviews, and mentor teammates.

As a Senior AI/ML Engineer, you will manage the AI/ML lifecycle, develop predictive models, conduct anomaly detection, and integrate AI capabilities into the Confido platform.

Design, develop, and deploy end-to-end machine learning and large language model solutions. Perform data discovery, cleaning, preprocessing, and visualization. Build/train LLMs and Agent+RAG systems, collaborate with stakeholders and AI teams, and apply graph-based analysis to support enterprise integration and analytics.

Build large-scale user-signal feature pipelines and production machine learning models for advertising targeting, bidding, and ranking. Own the full lifecycle from data preparation and feature engineering through training, deployment, monitoring, online serving, and A/B testing. Develop identity prediction, embeddings, intent, and conversion models; integrate outputs into real-time ads systems; improve signal quality; and provide technical leadership across Ads, Data, and Infrastructure teams.

Own product strategy and roadmap for Roku's recommendations platform, drive experiments and KPI-driven decisions, collaborate with engineering, ML, data science, UX and business partners, and deliver measurable engagement and monetization outcomes across multiple surfaces.

Build and train transformer models for plant intelligence, owning data curation, architecture, training strategies, evaluation, and production deployment. Develop robust model lifecycle systems and training and evaluation infrastructure for rapid iteration. Drive machine learning projects end to end, from data collection through production, while working independently on a small team. Apply strong software engineering and scientific skills to maintain practical ML systems.

Develop high-performance, large-scale machine learning services for a programmatic advertising platform. Build and deploy AI models and data-driven algorithms for real-time ad scoring, ranking, and bid optimization. Work with data scientists and cross-functional teams on scalable data analysis, model development, production rollouts, troubleshooting, and continuous optimization using agile methods.

Leads the design, development, deployment, and governance of production machine learning and GenAI systems on Azure Databricks. Establishes MLOps standards, builds MLflow and CI/CD foundations, architects RAG and LLM solutions, monitors model performance and cost, and mentors ML and data engineers. Partners with product, risk, governance, legal, and security teams to ensure models are reliable, explainable, fair, documented, and compliant.

Research and develop machine learning systems, algorithms, prototypes, and data-fusion solutions for complex sponsor problems. Responsibilities include data selection and modeling, statistical analysis, anomaly detection, visualization, software integration, technical reporting, collaboration with sponsors, and supervision of junior staff. The role supports classified defense and intelligence applications and is performed onsite in State College, Pennsylvania, or Reston, Virginia.