Top Machine Learning Jobs

Early-career ML engineer builds data pipelines and trains scientific ML models (surrogate and physics-informed), develops visualization/dashboard tools, runs experiments, and hardens prototypes for production while collaborating across engineering teams.

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.

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.

Drive applied ML research and build data-driven models for LLMs, multi-modal vision, GNNs, NLP, recommender systems and ranking. Write production-quality code, analyze data, scope and independently solve moderately complex problems, and collaborate cross-functionally.

Design, train, test, deploy, and maintain machine learning models for defense applications. Collaborate with data engineers, data scientists, architects, and product owners to implement AI use cases, guide clients on ML algorithms and tools, and drive mission-focused solutions.

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: design and implement ETL, data modeling, predictive models, anomaly detection, distributed data pipelines, and executive-ready visualizations. Drive business development, mentor teams, and deliver client-facing solutions in hybrid/onsite environments.

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.

Develop and improve machine learning models and infrastructure for humanoid robot manipulation. Monitor and evaluate model performance on real robots, and collaborate on the ML software stack supporting manipulation, navigation, locomotion, and perception.

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, train, and implement machine learning models and data-driven solutions using Python and R. Perform statistical analyses, data wrangling, and collaborate with cross-functional teams to deliver predictive analytics and actionable insights for federal clients.

Serve as the liaison between mission stakeholders and ML teams: elicit and document requirements, translate mission problems into user stories and technical specs, define model acceptance criteria, support UAT, maintain the MaaS product backlog, and communicate AI/ML concepts across technical and non-technical teams to ensure mission-relevant outcomes.

Design, build, and optimize LLM and RAG pipelines for classification, summarization, Q&A, and retrieval. Develop embeddings, vector storage, retrieval logic, and LLM orchestration. Apply ML/DL frameworks, traditional ML, cloud services, containerization, and MLOps to produce secure, reliable production solutions in collaboration with cross-functional teams and mission SMEs.

Conduct ML research and applied work on LLM trust and safety (prompt injection, alignment, privacy, interpretability). Design and run experiments, read/write research papers, fine-tune and evaluate deep models, and implement research code using PyTorch/HuggingFace tools. Collaborate with engineers to productionize research; related student research topics may be considered.

Design, build, evaluate, and deploy predictive ML models across regression, classification, NLP, computer vision, and time-series problems. Perform data cleaning, feature engineering, EDA, model selection, tuning, explanation (SHAP, feature importance), and collaborate with engineers and stakeholders to operationalize and monitor models in production.

Build and deploy large-scale ML systems for search, ranking, and personalization. Process billions of events and petabytes of logs into consumable features, train transformer and embedding models, serve models in real time, track impact metrics, and collaborate cross-functionally while mentoring engineers.

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.

Design and build machine-learning-driven robotic systems that operate in the real world, support portfolio company growth, influence product and technical direction, and advance company and career outcomes.

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.

The Machine Learning Engineer will design and implement ML pipelines and productionize models, collaborating with scientists and engineers on scalable systems.

As a Senior Machine Learning Research Engineer, you will research and implement advanced audio ML models, develop inference algorithms, and collaborate with the Operations team to enhance model quality.

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.