Top Machine Learning Jobs

As an ML Engineer, you'll deploy models and create tailored AI solutions for clients, ensuring impactful integrations and customer success.

The ML/LLM Engineer will optimize pipelines for ML and LLM systems, fine-tune models, collaborate on healthcare applications, and enhance model quality and safety.

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.

Develop and deploy lightweight machine learning models for edge AI, working on hardware integration and optimization techniques while mentoring junior engineers.

The Machine Learning Engineer will develop and optimize models, manage datasets, enhance training pipelines, and collaborate across teams to innovate in autonomous vehicle technology.

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.

As a Machine Learning Researcher, you will research and experiment with new architectures, optimize models, and validate findings through rigorous experiments, enhancing AI models for clients.

Seeking a Machine Learning Engineer to build models from idea to delivery, maintain production models, and develop system architecture while collaborating on data-driven strategies.

Lead design, development, and productionization of advanced ML models and data pipelines. Drive architecture, experimentation, validation, and best practices while mentoring ML engineers and delivering scalable, business-driving AI solutions.

Lead design, development, and deployment of advanced machine learning models and infrastructure. Own architecture, data pipelines, experimentation, validation, and team technical leadership to drive AI-driven product features and business value.

As a Senior Software Engineer, you'll build AI features focused on LLMs and RAG systems, collaborating across teams to ensure robust performance.

The Machine Learning Engineer will develop software solutions, optimize data pipelines, and deploy machine learning models using cloud platforms and best practices.

Four-month, full-time research fellowship focused on ML systems and performance. Fellows build and evaluate infrastructure-heavy projects (e.g., CPU simulators, accelerator backends, large-scale distributed training), use external models/APIs, receive mentorship, funding for compute, and aim to produce public research outputs.

Lead end-to-end ML projects for fraud detection: develop, deploy, monitor, and iterate production models and experimentation frameworks. Improve model performance, build scalable ML pipelines, collaborate with Product/Engineering/Risk, and champion testing, documentation, and model-monitoring best practices to ensure robust, high-impact solutions.

The Machine Learning Researcher at Virtu will develop and implement innovative deep learning models to solve complex market problems, collaborating with cross-functional teams and directly impacting business outcomes.

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.

Hands-on ML internship working on real production ML projects with mentor guidance. Build and optimize ML models and workflows using a large GPU cluster for noisy financial data; collaborate with engineers and attend ML-specific training and technical interviews.

Develop, test, and maintain applications using Java, Python, or Node.js. Write clean, efficient code, debug and optimize, collaborate with cross-functional teams, use data structures/algorithms, work with SQL/NoSQL databases, participate in code reviews, and stay current on AI/ML concepts, agents, and automation tools.

The Machine Learning Researcher will develop AI agents for private equity-backed workflows, focusing on ML cycle efficiency and decision-making.

Product-oriented senior ML role to build, deploy, and operate models and ML systems (classification, extraction, entity resolution, clustering, ranking, anomaly detection, forecasting, LLM pipelines). Define datasets, labeling, evaluations, monitoring, and collaborate with engineering and product to ship production-quality training/inference pipelines and model-serving infrastructure.

Build and own production applied-AI systems focused on information retrieval and LLM pipelines. Design experiment frameworks and evaluation pipelines, optimize for metrics (precision, recall, F1), run benchmarks, perform failure analysis, and iterate to improve real-world system performance at scale.

Develop spatio-temporal machine learning models for automated driving, collaborate with experts on algorithm design, and ensure production-level implementation of perception systems.

Lead and grow a small ML engineering team while staying hands-on: design and ship GenAI features, build agentic LLM systems (NER, classification, retrieval, summarization), partner with PM and engineering, scope ambiguous problems, and reduce COGS through ML automation.

Develop autonomy software for mining vehicles, including perception, SLAM, motion planning, control, sensor fusion, localization, and mapping. Integrate LiDAR, cameras, radar, IMU, and GNSS while building embedded real-time systems. Create simulation, logging, and data pipelines, and validate autonomy software through bench, rig, and field testing. Collaborate with hardware, controls, and systems engineers to deliver safe, reliable vehicle autonomy.