Top Senior Machine Learning Jobs

Design and build custom machine learning and deep learning solutions from first principles. Architect data models and pipelines, write modular maintainable code, implement tests and CI/CD, collaborate with engineers and analysts, and tune/optimize algorithms on on-premise infrastructure.

Lead the end-to-end ML execution: build and operate data pipelines, training and evaluation systems, and scalable inference. Fine-tune and optimize large models (LoRA/QLoRA/SFT/DPO/distillation), ensure GPU efficiency, monitor production quality, collaborate with product and application engineering, and rapidly ship reliable, safe ML solutions that translate research into production.

Design, train and validate supervised ML models on multi-billion claim records to score fraud risk; engineer features in-warehouse; deploy and monitor models in production; provide human-readable rationale and claim-level evidence; collaborate with subject-matter experts and stakeholders to ensure actionable, explainable outputs.

Build production decision systems that estimate causal effects and uncertainty, learn from interventions, optimize constrained policies (pricing, inventory, promotions), evaluate counterfactuals and challengers, and deploy safe, automated ML-driven business decisions to generate measurable economic lift.

Lead integration of AI/ML (LLMs, RAG, generative workflows) into a cloud-native full‑stack platform. Architect serverless backends, optimize data pipelines, modernize legacy PHP APIs, maintain CI/CD, mentor engineers, and support secure, defense-constrained deployments.

Lead ML engineer responsible for designing and building scalable data and ML systems for LLM development. Own data pipelines, human-in-the-loop labeling, synthetic data generation, automated evaluation, and dataset quality measurement. Collaborate with researchers and engineers, provide technical direction, and mentor teams to link dataset composition to model performance.

Design, build, and deploy scalable AI and machine learning solutions, leading full AI lifecycle, collaborating with teams, and ensuring systems align with client needs.

Design, implement, and optimize ML and deep learning models; preprocess and prepare large datasets; integrate models into production; monitor and maintain model performance; collaborate with engineers and stakeholders; stay current on ML/AI advances.

Design, implement, and optimize machine learning and deep learning models; preprocess and analyze large datasets; integrate models into production; monitor performance and troubleshoot; collaborate with engineers, data scientists, and stakeholders; stay current with ML/AI advances.

Own and build end-to-end matching and ranking systems for out-of-home advertising: design candidate generation, re-ranking, geospatial scoring, data warehouse and feature pipelines, evaluation infrastructure, low-latency ranking APIs, and model monitoring with drift detection and retraining triggers.

Own the ML lifecycle for LLMs applied to manufacturing data: fine-tune and deploy models for document classification, information extraction, table parsing, and multi-page reasoning; build annotation tooling, active learning, and synthetic data pipelines; develop evaluation frameworks; collaborate on AI platform roadmap and optimize model accuracy and production health.

Lead and grow a team of ML engineers building production ML systems for Fetch's Ad Platform. Translate product strategy into roadmaps, guide design and implementation of models for ranking, targeting, bidding and forecasting, set metrics and experimentation, enforce architectural and operational standards, manage trade-offs across performance and reliability, and drive cross-functional alignment to improve advertiser and member outcomes.

Lead and grow a distributed ML engineering team that runs experiments, ships production models, and partners with Risk and platform teams. Balance research bets and delivery, enforce rigorous evaluation and reproducibility, mentor engineers, own roadmap trade-offs, and represent results to stakeholders while improving model performance and production reliability.

The role involves designing, training, and evaluating ML models, optimizing performance, and collaborating with teams to implement AI technologies in products.

Lead development of internal Python SDKs and agentic AI workflows, build scalable FastAPI ML services, maintain CI/CD with Jenkins/GitLab Runners, and use Galileo for model evaluation and observability to productionize autonomous systems.

The Senior Consultant in AI will design, build, and deliver AI/ML solutions on Azure, engage with clients, and develop project components independently.

Lead the development of machine learning methods for AI-native metal manufacturing systems, focusing on integrating physical data with physics-based simulation.

Own end-to-end matching and ranking systems for out-of-home advertising: design candidate generation and re-ranking models, build and manage data warehouse and pipelines, implement evaluation and monitoring, publish low-latency ranking APIs, and maintain model observability and retraining workflows.

The Senior Machine Learning Engineer will automate healthcare compliance workflows using AI to process unstructured data, build multimodal AI pipelines, and develop extraction systems, ensuring high reliability and operational efficiency.

The Senior ML Systems Engineer will build and maintain the training framework for large-scale language models, focusing on distributed training and performance optimization.

The role involves designing de novo frameworks for molecular engineering, collaborating across teams to optimize data-driven biological experiments and integrating ML models with experimental systems.

The Senior Machine Learning Engineer will manage the full ML lifecycle, collaborating with teams to develop scalable systems that improve product decisions and empower monetization strategies.

Design, train, and deploy multimodal machine learning systems that fuse 3D/vision, simulation, tabular, and text data. Own end-to-end model lifecycle from architecture and training to evaluation, production deployment, and monitoring, and build agentic workflows and retrieval/embedding capabilities to support underwriting and mitigation decisions.

Responsible for developing machine learning capabilities, partnering with Data Scientists, and enhancing customer experiences through AI solutions.

Lead the end-to-end machine learning lifecycle for AI products, managing data pipelines and model development to optimize performance and accuracy in production.