Top Senior Machine Learning Jobs

Develop and tune ML models, productionize and deploy strategies for ML systems, and align cross-functionally to communicate performance metrics.

Lead ML/AI analytics across STARZ telemetry and operational data to detect anomalies, forecast trends, and surface actionable signals. Build repeatable signals frameworks, apply anomaly detection/classification/clustering/time-series models, leverage generative AI/LLMs for summarization and RCA, define alerting and escalation, partner with engineering and product, and mentor junior analysts.

As an ML Engineer at Luma AI, you will design high-performance model serving pipelines, manage GPU resources, and optimize CI/CD for large-scale AI systems.

The role involves developing advanced machine learning algorithms, solving complex optimization problems using reinforcement learning techniques, and leading technical projects while optimizing solutions for operational efficiency.

Develop, iterate, and deploy machine learning models and agent systems for investment decision-making. Design experiments, build reproducible feature-generation and evaluation workflows, and work with large-scale financial and alternative datasets to identify predictive signals and improve production performance.

Develop AI/ML models for 3D simulation and geology prediction, refine data pipelines, and integrate deep learning tech with product backend.

As a Senior Machine Learning Engineer, you will architect a scalable ML platform, collaborate across teams, and ensure best practices for ML workflows and operations.

Lead ML work for send-time, channel, and propensity optimization to increase engagement. Design and ship recommendation/sequential-decision systems, ensure experimental rigor, own production models end-to-end, and mentor a small ML team while partnering with product, engineering, and growth.

Seeking a Senior Data Scientist/ML Engineer to develop and deploy Machine Learning algorithms, collaborate with teams, and assist federal clients in their digital transformation.

Design, build, deploy, and operate scalable production ML and AI systems (batch and real-time) to predict KPIs and optimize provider network operations. Own end-to-end model lifecycle including feature engineering, training, deployment, monitoring, CI/CD, and experiment tracking. Collaborate with product, engineering, clinical and business stakeholders, implement production-quality code (Python, Spark, SQL), and integrate LLMs, RAG, and other AI capabilities to deliver measurable business and clinical outcomes.

As a Senior ML Engineer, you will develop ML pipelines, work with language models, and build agentic AI systems while implementing MLOps practices.

The Senior Machine Learning Engineer will develop end-to-end ML systems connecting perception inputs to driving behavior, enhance model performance, and mentor juniors.

Develop, deploy, and monitor machine learning and analytic capabilities for defensive cyber operations. Ingest and transform multi-source data, build data pipelines, validate operational models, collaborate with analysts to deliver actionable insights, and document processes for reproducibility in high-security environments.

The role involves designing scalable distributed systems using Scala, enhancing feature stores for performance, and collaborating with ML engineers to resolve challenges.

Designs, implements, and deploys scalable AI/ML solutions: selects algorithms, engineers features, trains and tunes models, integrates models into production, monitors performance, mentors team members, leads design reviews, and communicates technical strategy to stakeholders.

The role involves developing ML-based planning algorithms for autonomous trucks, ensuring safety checks, real-time operation, and keeping up with research. Requires strong communication and collaboration.

The Machine Learning Performance Engineer will build training and inference pipelines, enhance deep learning frameworks, improve performance, and collaborate with researchers and engineers to optimize trading systems.

Lead design, implementation, and deployment of scalable AI/ML systems for aerospace and defense. Develop RAG, embedding/retrieval pipelines, transformers, GANs, RL and MPC-aligned models. Use GPU/HPC for real-time decisioning, mentor engineers, ensure safety, validation, regulatory and cybersecurity compliance, and support integration and prototyping.

Research and develop ML and statistical algorithms for networking, edge computing, cybersecurity, and autonomy. Build and evaluate simulation-driven solutions, lead subsystem software development, create automated tests, and contribute to proposals and technical plans while presenting results to customers and senior staff.

Lead a small ML engineering pod to turn research into production: design and operationalize training pipelines and low-latency inference services for computer vision, document intelligence, and predictive models. Drive R&D execution, benchmarking, prototyping, migrations from frontier models to fine-tuned/open-source solutions, and mentor early-career engineers while aligning research with product timelines.

Lead end-to-end development of fraud and risk ML systems: define problems, engineer features, build and evaluate models, collaborate with product and engineering, deploy with ML engineers, and monitor production performance to detect drift and trigger retraining.

Drive applied ML research and build data-driven models for LLMs, multi-modal vision, GNNs, NLP, recommender systems and ranking. Write production-quality code, analyze data, scope and independently solve moderately complex problems, and collaborate cross-functionally.

Lead end-to-end applied ML: turn problems into datasets, experiments, and production models. Build Python production systems for data collection, feature extraction, scoring, APIs, and observability. Improve LLM systems, handle untrusted data safely, evaluate models and backtests, and work directly with stakeholders to prioritize and iterate.

Lead AI/ML consulting engagements, build and deploy machine learning models and ETL pipelines, analyze large datasets, design data architectures, deliver visualizations, drive business development, and manage client-facing teams in high-stakes situations.

The Staff ML Engineer will lead the architecture and development of AI/ML features, oversee complex systems, mentor teams, and enhance product experiences through ML solutions.