Top Senior Machine Learning Jobs

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 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.

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 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.

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.

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.

Lead design and productionization of next-generation search ranking and recommendation systems using transformers, LLMs, GNNs, and deep learning. Build personalized, real-time retrieval and ranking models, collaborate cross-functionally to run experiments and A/B tests, and mentor engineers on MLOps and model development to improve retailer discovery and search relevance.

Design, build, and productionize personalized recommendation models and LLM-based experiences for Spotify's Home feed. Own model training, fine-tuning, evaluation, A/B testing, inference optimization, and ML platform/data pipeline improvements. Drive technical direction, mentor engineers, and collaborate with cross-functional partners to deliver scalable, cost-efficient personalization at global scale.

Senior Machine Learning Engineers will develop ML products and lead projects end-to-end—from initial concept through final launch—across S32 portfolio companies. Candidates should drive product direction, have strong ML engineering and productization experience, and make significant business impact.

As a Senior Machine Learning Scientist, you will develop multimodal foundation models using large-scale single-cell datasets to advance drug discovery, applying deep learning methods and collaborating with biologists and engineers.

Lead architecture and productionization of scalable ML/LLM systems for legal products. Build LLM applications (RAG, agents), hybrid search, APIs, streaming/data pipelines, containerized deployments, and observability. Partner with data scientists to turn models into reliable, low-latency customer-facing services and establish reusable frameworks, best practices, and technical leadership across teams.

Lead post-training for LLMs (CPT, SFT, RL) focusing on RL methods; design and curate training and reward data; run and debug distributed training on mid-to-large GPU clusters; build evaluation and verifier pipelines; partner with product and business teams to deliver targeted model improvements quickly.

Build and design backend services, AI agents, and machine learning models. Collaborate cross-functionally to deliver AI features and maintain code quality.

The Senior Machine Learning Engineer will lead the perception stack, focusing on detection, tracking, classification, and sensor fusion for targeting systems, with responsibilities including model development and real-time optimization on embedded devices.

Build and improve AI/ML systems and data pipelines for a B2B SaaS platform; shape product and technical strategy; work with customers; mentor team members; ensure secure, robust, and scalable infrastructure.

As a Senior Software Engineer, you'll develop machine learning models and software applications, translating technical requirements into solutions while ensuring excellence in product delivery.