Top Senior Machine Learning Jobs

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, deploy, and maintain large-scale ML workflows and knowledge graph services. Build production-ready LLM and RAG-based search pipelines, data ingestion/preprocessing, feature stores, monitoring, and security. Collaborate with cross-functional teams, document systems, research advanced ML/NLP techniques, optimize knowledge graph algorithms, and mentor junior engineers.

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.

Lead development and production deployment of ML products: perform exploratory analysis, build and evaluate models, design experiments and metrics, leverage generative AI and graph-based methods, collaborate with product and engineering, influence strategy, and maintain robust CI/CD and platform practices.

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.

As a Senior Machine Learning Engineer, you'll design and implement large-scale E2E driving models, enhance data workflows, and collaborate with cross-functional teams to advance autonomous driving technologies.

As a Machine Learning Scientist at Nucs AI, you will develop innovative ML methods for medical image analysis, collaborate with clinicians, and contribute to clinical validation studies, impacting cancer diagnostics.

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.

Develop and deploy machine learning solutions for autonomous truck applications: EDA, deep learning model development, embedded deployment, data ingestion/curation, analytics, visualization, technical leadership, and process improvement across manufacturing and business domains.

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.

As a Senior Machine Learning Engineer at ZoomInfo, you will enhance core datasets through ML/NLP solutions, manage entity resolution tasks, and drive cross-functional impact by mentoring teams and collaborating on ML KPIs.

Develop and implement AI/ML solutions for cyber threat detection, involving model design, deployment, and performance optimization, along with mentoring responsibilities.

Lead and grow a team of ML, backend, and data engineers to design, deploy, and operate production-scale personalization and recommendation systems. Set technical direction, partner with Product and Data Science, mentor engineers, prioritize delivery, and champion adoption of modern ML techniques including large language models and generative AI to improve user personalization.

Build and maintain scalable ML and generative AI platforms and backend infrastructure; deploy and manage production applications; create services exposing ML/AI products; implement RAG solutions and vector databases; integrate LLM orchestration and foundation model APIs; collaborate cross-functionally and mentor teammates.

Lead development of large-scale ranking, personalization, and recommendation systems for Roku's entertainment assistant. Design and deploy ML models (LLMs, transformers, RL, bandits), build feature platforms and data pipelines, run online A/B experiments, and mentor engineers while balancing engagement, retention, relevance, and monetization objectives.

Build and own LLM systems and infrastructure for Overtone's product: prompt architecture, evaluation pipelines, model experimentation, serving, fine-tuning (SFT/DPO/RLHF), vector DBs and personalization, observability tooling, and lightweight matching models. Collaborate cross-functionally to deploy reliable, auditable AI features in production.

Design, build, and deploy high-throughput ML and LLM-powered personalization and agentic systems at consumer scale. Process petabyte-scale data, optimize real-time inference, implement resilient fallback paths, and balance latency, cost, and model quality while collaborating across engineering and product teams.

Own end-to-end CTR/CVR prediction models in a real-time bidding system (sub-100ms): modeling, feature pipelines, offline and online evaluation, A/B testing, experiment debugging, and post-launch ops. Hunt for new predictive signals, build tooling for experiment debugging and signal discovery, collaborate with data and pipeline teams, and set technical direction for NEXT's ML stack.

Lead architecture and development of distributed, large-scale ML systems for Merlin Bird ID and related platforms. Build training pipelines, production deployments, APIs, and front-end integrations. Mentor engineers, set technical direction, ensure scalability, performance, reliability, and collaborate with engineers, scientists, and domain experts to deliver global conservation technology.

Lead design, build, and modernization of recommendation systems integrating backend engineering, data pipelines, and LLMs. Develop, test, deploy, and maintain scalable microservices and MLOps solutions, mentor engineers, collaborate with product teams, and drive system design, monitoring, and retraining pipelines for production ML.

The Senior Machine Learning Engineer will design, build, and deploy scalable ML systems, collaborating closely with teams to optimize AI processes and mentoring juniors.

The role involves designing and implementing machine learning systems for knowledge representation and temporal reasoning, focusing on organizational intelligence. Responsibilities include building LLM-powered pipelines, developing memory algorithms, and researching various ML techniques.

Own and scale ML tasks for multi-modal perception in autonomous vehicles, analyze real-world system behavior, monitor production models, develop automated detection and debugging tools, and maintain safety-critical metrics and longtail issue tracking.