Top Machine Learning Jobs

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.

Design and develop machine learning systems for construction robots, including scalable infrastructure, computer vision models, and data analysis for performance improvement.

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.

Build, maintain, and improve Swayable's AI/ML and scientific computing systems. Productize machine learning and data-analysis workflows, optimize high-performance distributed and parallel computing, and collaborate with engineers, data scientists, and researchers to deliver scalable, testable solutions and new product features.

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.

Build and maintain scalable backend services, APIs, and data pipelines for computer vision and LLM workflows. Integrate model serving, ensure reliability, performance, and observability, collaborate with ML and Solutions Engineering, and support production deployments and occasional on-site testing.

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.

Design, develop, and deploy AI/ML solutions for wired and wireless signaling data. Implement and benchmark neural networks, optimize algorithm performance, support Agile software development, collaborate cross-functionally, and mentor junior staff. Work requires eligibility for TS/SCI with polygraph and a STEM bachelor's degree.

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.

Lead the transition from teleoperation to autonomy in robots by developing Vision-Language-Action models and deploying ML systems. Collaborate on data processing and model integration for robotics applications.

The role involves preparing traffic data, developing and deploying machine learning models on Google Vertex AI, and collaborating with teams to improve project workflows.

The Forward Deployed ML Engineer will integrate AI/ML models into clinical software, assist in model fine-tuning, and engage with clients to ensure success in healthcare deployments.

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.

Lead collaboration to improve ML workloads on cloud and self-driving cars, optimize models, and mentor junior engineers.

Analyze workloads for hardware mapping, design machine learning solutions, and collaborate with cross-functional teams on high-performance architecture.

Build and enhance ML systems for custom model training, oversee projects from data intake to model delivery, ensure model quality at scale, create evaluation frameworks, and collaborate with engineers.