Top AI & Machine Learning Jobs

Lead production-grade ML systems: build data pipelines, training and evaluation workflows, and scalable GPU inference. Fine-tune and adapt large models (LoRA, QLoRA, SFT, DPO, distillation), optimize deployments for latency/cost/reliability, and collaborate with product and engineering to ship robust, observable model-driven features.

Co-develop an EIC Pathfinder proposal focused on trustworthy cognitive AI, including causal reasoning, abstraction, planning under uncertainty, methodology, related work, and evaluation design. The researcher will help build a working TRL 3/4 system, develop benchmarks, and collaborate closely with a startup team through submission and subsequent project activities.

Research and deploy reliable robotic manipulation policies for live operations. Responsibilities include developing policies across multimodal data, debugging transformer and diffusion models, operating real-robot data flywheels, and optimizing inference for approximately 10 Hz performance on edge hardware.

Lead design, development, and deployment of LLM-powered solutions and autonomous procurement agents on the GEP AI platform. Advise stakeholders, run workshops, configure platform ontologies and data pipelines, optimize models for procurement tasks, and oversee cross-functional implementation to drive automation and improved procurement decision-making.

Build and maintain clinical AI benchmarks, evaluation infrastructure, grading systems, and production harnesses. Convert consented patient cases into de-identified evaluation and training data through clinician review workflows. Lead clinical evaluation studies from protocol design through publication, collaborate on reward design and data curation, and develop agentic evaluations covering tool use, long-horizon tasks, memory, records, labs, and escalation behavior.

Design, implement, and maintain scalable ML pipelines for training, deployment, and monitoring. Automate data preprocessing, feature engineering, and validation. Productionize models with data scientists and backend engineers, ensuring reliable, secure, and efficient ML infrastructure and optimized training and inference performance.

Collect, clean, and analyze sports data; develop machine learning and predictive models; create visualizations and reports; collaborate with data and sports strategy teams; and monitor advances in sports analytics.

Instructor-led AI and machine learning training covering theory, data preprocessing, algorithms, model building, analytics, real-time projects, case studies, and practical business and IT applications. Participants use Python, TensorFlow, and Scikit-learn and receive certification upon successful completion.

Founding engineer responsible for architecting and building core product systems, writing scalable Python backend code, designing and maintaining AI/ML infrastructure, instituting best development practices (testing, CI/CD), collaborating on product roadmap, and mentoring future engineers.

Own the analytical layer for autonomous-driving model evaluation and release decisions. Analyze simulation and on-road experiment data, improve performance metrics and scoring methods, identify measurement gaps, apply statistical rigor, and translate investigations into actionable recommendations. Partner with Data Science, Validation, Product, engineering, and OEM teams to strengthen evaluation coverage, ground truth, quality investigations, and weekly model-release feedback loops.