Top AI & Machine Learning Jobs

Own production-grade analytics pipelines from design through maintenance, build dbt-based analytical models using Kimball dimensional modeling, and deliver reliable data features. Partner with Product, Engineering, and Data Science teams to translate retail signals into business insights. Establish standards for monitoring, documentation, reproducibility, and code quality. Use AI tools to accelerate development and prototype conversational data experiences for natural-language retail data exploration.

Provides technical leadership in data science and AML data governance. Translates business and financial-crime risk problems into scalable data solutions using Python, SQL, statistics, advanced analytics, data pipelines, and visualization. Partners with business, engineering, and analytics teams; oversees regulatory reviews, data controls, risk remediation, and governance initiatives. Communicates complex insights to senior stakeholders, supports enterprise programs, mentors colleagues, and drives continuous process improvements.

Designs, develops, and deploys enterprise Generative AI solutions using LLMs, semantic search, and AI orchestration. Builds monitoring systems, improves AI architecture, supports prompt engineering and evaluation, reviews code, and mentors engineers. Collaborates with product leaders and stakeholders to deliver secure, scalable, customer-facing AI applications. Requires Azure OpenAI, Azure AI Search, Semantic Kernel, DevOps, CI/CD, testing, and advanced Python expertise.

Build and own Custral’s AI agent runtime and full-stack product surfaces. Develop tool gating, context assembly from Postgres and OpenSearch, model selection, tracing, evaluation harnesses, and regression suites for LLM systems. Ship React, Node.js, and Express functionality to real users, improve agent capabilities, and ensure model and prompt changes deliver measurable customer impact. Work independently in an early-stage, in-person New York City environment.

Conduct a semester-long applied AI research project with an AfterQuery mentor in areas such as data quality, post-training, agent and foundation-model evaluation, or public benchmarks. Responsibilities include formulating research questions, designing experiments, developing datasets and evaluation methods, analyzing results, documenting reproducible work, and co-authoring a publication. Scholars receive mentorship, engineering support, annotation resources, and compute access.

Conduct independent AI research for predictive toxicology and drug safety. Develop novel deep learning architectures, multimodal models, representations, uncertainty methods, and mechanistic reasoning approaches for heterogeneous biological and chemical data. Design rigorous experiments, benchmarks, ablations, and generalization evaluations while collaborating with engineering and scientific teams to translate validated research into scalable modeling systems. Contribute to scientific strategy, publications, collaborations, and regulatory validation.

This Machine Learning Engineer L2 position is an incomplete job requisition template. It does not provide specific responsibilities, qualifications, technologies, education requirements, or measurable role details. The position is described as remote-first at Twilio, with occasional travel potentially required for team or project meetings.

Leads Nuro’s Software Operations organization, overseeing AI training and evaluation data, autonomy regression analysis, release validation, operational KPIs, and root-cause investigations. Manages large-scale data-labeling vendors, including quality, throughput, SLAs, budgets, and pricing. Partners with engineering, machine learning, data science, simulation, and product teams to build evaluation datasets, improve autonomy performance, and scale operational processes and teams.

Leads the strategy, architecture, development, deployment, monitoring, and optimization of an agentic AI platform. Transforms products, processes, and workflows into scalable AI-enabled capabilities; establishes standards for performance, security, governance, and reliability; evaluates models and orchestration frameworks; partners cross-functionally on technical roadmaps; and builds and mentors high-performing engineering teams.

Serve as technical lead in pre-sales: architect and demo LanceDB solutions for RAG, vector search, and ML pipelines; run discovery sessions, build POCs, onboard partners, and relay customer feedback to product and engineering.