Top Senior Machine Learning Jobs

Lead design, training, and deployment of large-scale ML models (LLMs, diffusion, GNNs). Own end-to-end training pipelines, distributed training, evaluation, and inference architecture. Collaborate with data and infra teams, drive reproducible model development, and provide technical leadership and mentorship from research through production.

Develop and implement reinforcement and supervised learning algorithms for robotic manipulation, build simulations and domain randomization, optimize model architectures and data pipelines for sample efficiency, and deploy, evaluate, and debug policies on physical robots.

The Staff ML Engineer will define and build ML capabilities, focusing on production systems, strategy, and infrastructure in a cybersecurity context.

As a Senior Machine Learning Engineer (Tech Lead), you'll lead the ML technical direction, design architectures for loco-manipulation, mentor team members, and manage cross-functional partnerships while writing production-quality code.

The role involves deploying and optimizing LLMs, designing the ML serving stack, and ensuring high-performance GPU services for production readiness.

As a Staff Machine Learning Engineer, you will design, build, and deploy ML systems, optimize performance, and collaborate with teams to integrate AI solutions.

The Staff AI/ML Engineer will develop and prototype AI/ML algorithms, analyze datasets, and mentor team members in an interdisciplinary environment.

Design and oversee the development of robust data platforms for training and evaluating ML models, focusing on autonomous driving. Collaborate with multiple teams and optimize data processing pipelines.

The Senior ML Engineer will automate component placement on PCBs, working on R&D and production using ML and optimization techniques.

Design and build ML infrastructure and models for ad performance: feature engineering, batch and real-time training and serving, low-latency evaluation, production performance optimization, cross-functional collaboration, and monitoring model quality at large scale.

Design, build, and operate large-scale ML systems for ad performance: feature engineering, model training/deployment (batch and real-time), evaluation, monitoring, and production optimization while collaborating with cross-functional teams.

Designs, trains, evaluates, deploys, and monitors scalable AI/ML models and data pipelines to meet federal customer requirements. Leads model architecture, testing, integration, and iterative improvement while ensuring data and model quality and presenting results to stakeholders.

Collaborate to scale self-driving systems, build training frameworks, profile model performance, and evaluate emerging technologies for training and inference.

Design and productionize ML models and infrastructure to assess autonomous vehicle behavior in simulation and field. Operate large-scale ML and data systems, build simulation workflows, combine human judgments with ML for training and evaluation, and collaborate across teams to deploy state-of-the-art models and improve model quality.

The Staff Machine Learning Engineer will lead ML strategy, architect AI infrastructure, mentor engineers, and develop impactful AI systems.

Lead ML efforts for behavior and planning: design, train, and deploy state-of-the-art models (prediction, decision-making, RL/imitation, generative, transformers), own full model lifecycle from data to onboard inference, generalize behavior across geographies and platforms, collaborate cross-functionally, and mentor engineers.

Design and implement agentic systems using LLMs and agent frameworks, build multi-agent workflows and RAG pipelines, optimize scalable agent architectures, collaborate on experiments and feature integration, and ensure quality through reviews, testing, and documentation.

Lead development of a high-performance robotics simulation platform and its compiler stack. Design compute infrastructure and data flow for physics simulation and model training, optimize GPU codegen and JIT compilation, improve differentiable programming support, and contribute to open-source ML compiler projects while collaborating with simulation and robotics engineers.

Design, build, deploy, and operate production-grade ML systems and pipelines for a massive consumer audience. Own full lifecycle from data processing and feature engineering through training, validation, deployment, and monitoring. Drive ML infrastructure and architecture decisions, partner with product and data teams, optimize models against business metrics, and mentor engineers to ensure security, reliability, and compliance.

Design and build data and model layers for an accredited AI decision-support system: record matching, calibrated scoring, provenance-tracked retrieval and generation, secure ingestion pipelines, model packaging/serving, and instrumentation for audit and accreditation.

Lead design, development, testing, and deployment of cloud-enabled AI/ML systems (computer vision, sensor fusion, decision support) for maneuver, surveillance, and engagement missions. Evaluate model performance on operational datasets, optimize for robustness and cyber resilience, troubleshoot deployed systems, and produce technical documentation and reports for stakeholders.

Lead design, development, and production deployment of ML models and GenAI/LLM solutions to optimize clinical trial workflows. Build scalable pipelines, monitoring, and CI/CD for robust production use. Collaborate cross-functionally, mentor junior engineers, and communicate technical results to technical and non-technical stakeholders.

As an Applied ML Software Engineer, you will design and implement ML models, manage cloud infrastructure, and optimize backend systems for AI-driven content discovery and recommendations.

Lead development and deployment of credit, account-management, and fraud ML models using embeddings, transformers, and classical approaches. Own data pipelines, feature engineering from structured and unstructured sources, production monitoring, and cross-team integration while shaping technical strategy and mentoring Juniors.

The Staff ML Engineer will lead architecture decisions for ML infrastructure, operate real-time computer vision systems, and mentor engineers, ensuring high-quality ML deployment and operational excellence at SimpliSafe.