Top AI & Machine Learning Jobs

Leads firmwide adoption of AI tools in partnership with attorneys and paralegals. Translates legal workflows into scalable AI solutions, manages platform configuration, supports AI governance and risk practices, and develops communications and training programs. The role requires hands-on AI platform expertise, strong project management, legal workflow knowledge, stakeholder collaboration, and the ability to identify and address risks in AI-enabled legal work.

Build AI-training products from acquired codebases, workspaces, and databases. Develop reinforcement-learning environments, agentic task suites, evaluations, verifiers, benchmarks, and training datasets. Create reproducible pipelines using repository ingestion, test harnesses, Docker sandboxes, reward scripts, and QA tooling. Collaborate with AI labs and buyers, identify commercial opportunities, and protect sensitive data through strong security, privacy, licensing, and PII practices.

Defines enterprise architecture standards and reusable patterns for AI, agentic AI, and AI-enabled applications. Guides model selection, agent design, security, governance, observability, evaluation, and cost management. Partners with engineering, data, security, enterprise architecture, and business teams to move AI solutions from concept to production. Develops proof-of-concept implementations, reference architectures, and AI lifecycle guidance while ensuring alignment with enterprise technology strategy.

Lead development of an evaluation platform for agentic AI and LLM systems. Design benchmarking, human review, LLM-as-judge, monitoring, annotation, dataset versioning, and data quality workflows. Own technical architecture, production infrastructure, stakeholder reporting, and platform health while partnering with research, product, and platform teams. Mentor engineers and support scalable AI experimentation and deployment.

Leads the design, development, deployment, and validation of advanced machine learning systems for aerospace and defense applications. Responsibilities include developing deep learning, reinforcement learning, generative AI, computer vision, signal processing, and planning algorithms; architecting scalable CPU/GPU platforms; managing projects; mentoring engineers; and ensuring safety, performance, cybersecurity, and regulatory compliance. The role involves occasional travel and requires the ability to obtain and maintain a Secret U.S. security clearance.

Lead design and implementation of a production-grade LLM inference platform: deploy and serve models on GPU infrastructure, optimize latency and cost with quantization/batching/caching/routing, build infrastructure in Python or Golang, operate serving frameworks (vLLM, SGLang, TensorRT-LLM), and own the inference roadmap working with CTO and Product.

Develop advanced analytics, statistical models, machine learning and AI solutions supporting merchandising, marketing, supply chain, operations, real estate and other business functions. Partner with stakeholders to identify opportunities, create recommendations, scorecards and dashboards, define KPIs, and drive adoption of data products. Communicate technical insights through presentations, storytelling and reporting while collaborating cross-functionally to improve sales, margin, profitability and customer outcomes.

Develops real-time computer vision, image-processing, tracking, and state-estimation algorithms for directed-energy defense systems. Responsibilities include integrating vision pipelines with sensors, gimbals, and beam-steering hardware; optimizing CUDA and TensorRT workloads on NVIDIA Jetson platforms; processing EO/IR imagery; building simulation and playback tools; and supporting modeling, field testing, calibration, and algorithm validation.

Paid summer 2027 data science internship in EMC Insurance’s Strategic Analytics department. Responsibilities include collecting, cleaning, and preprocessing structured and unstructured data; creating modeling datasets; performing exploratory data analysis; identifying patterns and trends; researching emerging technology; and learning insurance operations, ratemaking, statistics, data management, and programming.

Conduct advanced research combining deep learning with subsurface physics. Develop AI architectures and neural operators for seismic modeling, inversion, and geologic interpretation; generate synthetic data; train, validate, and deploy models; apply uncertainty quantification and generative modeling; and connect geophysical outputs to reservoir properties. Collaborate with customers and domain experts, publish research, and move models into production for energy and subsurface applications.