Top Senior Machine Learning Jobs

Own the production serving path for multimodal AI models, including Rust inference runtime nodes, GPU kernels, routing, batching, streaming, quantization, observability, and low-latency optimization. Productionize research checkpoints and improve GPU utilization, numerical precision, latency, and cost while collaborating closely with researchers.

Lead quantitative model risk assessments and develop alternative modeling approaches for loan loss forecasting and ACL. Apply econometric, statistical, and machine learning methods, maintain model accuracy, produce transparent documentation, and communicate technical results to management, regulators, and stakeholders. Leverage cloud and open-source tools, collaborate with model development and risk teams, and drive continuous improvement in modeling practices.

Own the production lifecycle for machine learning and LLM-backed systems, including training and post-training pipelines, inference serving, model releases, monitoring, retrieval infrastructure, and cost and latency optimization. Partner with AI researchers and engineering teams to move prototypes into reliable production capabilities across customer tenants. Develop ETL and GraphQL features, enforce tenant isolation, and support deployments in regulated or customer-controlled environments.

The Senior Applied Scientist will design and optimize credit risk models using machine learning, collaborating with cross-functional teams to translate business problems into technical solutions and drive scalable risk decisions.

Build and deploy reliable AI agents that automate client workflows and integrate with enterprise systems. Responsibilities include mapping processes, designing automation roadmaps, developing evaluation frameworks and regression tests, monitoring agent performance, instrumenting audit trails, scaling systems to production, and leading workforce adoption and change management. The role also codifies reusable patterns across engagements and requires customer-facing collaboration, production AI experience, Python or TypeScript development, Secret clearance, and up to 10% travel.

Design, develop, test, evaluate, and deploy AI/ML models across generative AI, classification, prediction, computer vision, NLP, and reinforcement learning. Provide AI/ML engineering support using algorithms, software development, and cloud computing. The role requires five years of AI/ML development, cloud-based deployment, and Python or R experience, plus U.S. citizenship and an active TS/SCI clearance with polygraph.

Lead and contribute to Waymo’s planet-scale ML data infrastructure and inference platform. Partner with modeling teams to manage data quality, distributions, freshness, feature stores, and ML flywheels. Mentor engineers, lead complex cross-functional projects, and support scalable data stores, model hosting, bulk and online inference, and high-QPS production services.

Designs, deploys, and scales production AI agents and agentic workflows within enterprise Java and Spring applications. Establishes evaluation, monitoring, guardrail, safety, testing, observability, deployment, and operational standards for LLM systems. Leads architectural decisions across AI services and microservices, productionizes models, evaluates emerging technologies, mentors engineers, and influences technical strategy across teams.

Architects and develops secure, scalable AI/ML systems and MLOps infrastructure across distributed cloud environments. Builds model training, inference, monitoring, governance, adversarial robustness, federated learning, and multi-agent safety capabilities. Designs agentic workflows, data and ETL pipelines, model lifecycle automation, and high-performance serving systems. Evaluates reliability and security under real-world and adversarial conditions, collaborates with cybersecurity and cloud teams, and mentors engineering teams on advanced machine learning and secure development practices.

Develop machine learning models to predict printability of complex CAD geometries using image, thermal, simulation, and sensor data. Design and maintain structured and unstructured data systems supporting diverse data ingestion, storage, and retrieval. Deploy models into production in collaboration with preprint, process, and applications teams. Experience with geometric data, production software engineering, and Python or C++ is required; powder bed fusion and manufacturing data workflows are preferred.

Designs, deploys, and supports production AI/ML solutions across data ingestion, feature engineering, model training, MLOps, monitoring, and lifecycle management. Builds AWS cloud-native applications, generative AI systems, knowledge graphs, and real-time or batch pipelines. Partners with stakeholders on analytical solutions, ensures data quality and AI governance, leads technical design, and mentors engineers.

Design and operationalize AI/ML capabilities for secure DoD environments, including agentic workflows, production APIs, MLOps, model deployment, monitoring, validation, and continuous improvement. Integrate structured and unstructured data with Foundry applications and mission systems. Collaborate with engineering and mission teams, troubleshoot production systems, and apply responsible AI, security, governance, and model-management practices.

Lead the design, development, deployment, and monitoring of production AI/ML applications for dairy-farm intelligence. Responsibilities include data wrangling, feature engineering, model selection, experimentation, MLOps, agent evaluation, observability, fine-tuning, AI governance, and integration with cloud infrastructure. The role also drives AI adoption, establishes engineering practices, collaborates cross-functionally, and may build React and TypeScript user experiences.

Designs, develops, evaluates, and deploys machine learning models for sensor-data event detection, classification, and characterization in classified national-security environments. Builds end-to-end ML pipelines, integrates capabilities into operational software, evaluates failure modes, collaborates with scientists and analysts, and mentors junior developers. The role requires production-quality Python, deep knowledge of modern ML architectures, technical documentation, and active Top Secret clearance with eligibility for SCI and CI Polygraph.

Develop and deploy computer vision and machine learning models for semantic and instance segmentation, object and damage detection across 2D and 3D data. Responsibilities include training loops, dataset curation, labeling guidelines, evaluations, ablation experiments, model export, inference code, testing, profiling, and production support. Collaborate with research, backend, operations, and labeling teams while documenting experiments and improving model performance.

Develop and scale vision-language-action models for robotic manipulation, owning dataset construction, training recipes, distributed GPU infrastructure, evaluation, and on-robot validation. Build reproducible ML pipelines, optimize multimodal data processing and model training, diagnose failures across data and robot behavior, and translate research into production-quality systems. Collaborate across research, robotics, hardware, data, and operations teams while mentoring engineers.

Build evaluation infrastructure for Aircall’s AI voice, chat, and messaging agents. Responsibilities include designing benchmarks and metrics, training and fine-tuning TTS, ASR, and speech-to-speech models, developing automated regression and release gates, calibrating LLM-as-judge systems, analyzing failures, monitoring production quality and drift, and evaluating voice-specific scenarios such as accents, noise, barge-in, DTMF, latency, and tool failures.

Lead the design, development, deployment, and monitoring of production AI/ML applications for dairy farm intelligence. Responsibilities include data wrangling, feature engineering, model selection, experimentation, MLOps, agent evaluation, observability, fine-tuning, AI governance, and adoption enablement. The role also involves collaborating on React and TypeScript user experiences, translating ambiguous requirements into scalable systems, and communicating technical tradeoffs across product, engineering, and dairy science teams.

Lead design and productionalize ML algorithms and pipelines to detect account-sharing fraud in real time. Build scalable preprocessing, training, deployment, monitoring, and A/B testing solutions with cross-functional teams to optimize detection while minimizing user disruption.

Lead design and implementation of complex embedded and application software for signal processing and algorithm development (including machine learning) for EO/IR and electronic warfare systems. Provide technical leadership, mentor teams, produce design/project documentation, evaluate and optimize algorithms (C++), and collaborate with systems, hardware, and test engineers through full lifecycle development and customer technical reviews.

Architects, develops, and deploys high-performance machine learning systems across the full lifecycle. Responsibilities include large-scale feature engineering, online and offline pipelines, tree-based and deep learning models, low-latency serving, MLOps automation, model monitoring, and rigorous experimentation. The role requires production-grade software engineering, distributed computing, statistical expertise, and experience shipping ML systems in high-scale environments.

Designs and implements AI-enabled workflows for cyber and intelligence missions, including LLM-based agents, multi-step automation, API and data-store integrations, and production deployments in classified environments. Collaborates with mission users, analysts, developers, and leadership to translate operational workflows into reliable, secure capabilities. Requires Python experience, TS/SCI clearance with polygraph, and relevant AI/ML or software engineering experience.

Own the architecture and hands-on implementation of production MLOps systems, scalable inference services, model deployment pipelines, monitoring, and governance. Integrate machine learning into pharmaceutical manufacturing, process control, automation, and scientific or clinical applications. Collaborate with chemical engineers, computational biologists, and platform teams while establishing enterprise CI/CD and MLOps standards. This is a hands-on senior role requiring hybrid onsite work in Indianapolis.

Develop and optimize deep learning models for computer vision applications, focusing on neural network architecture, training, inference optimization, and production deployment. Build tools and infrastructure for model training, evaluation, and deployment in Linux and cloud environments. Research emerging machine learning techniques, integrate them into products, and collaborate with software engineers on customer-focused projects throughout the product lifecycle.

As a Senior Machine Learning Engineer, you will manage the entire lifecycle of safety-critical ML perception models, optimize performance, and ensure reliable deployment on embedded systems.