Top AI & Machine Learning Jobs

As a Senior Applied ML Engineer at Macroscope, you will design, build, and optimize ML/AI systems, leading the AI Code Review process and ensuring integration with backend systems.

Design, build, and own large-scale data pipelines and ML data curation for multimodal (text, image, audio, video) model training. Partner with research teams to ingest, label, filter, augment, store, and ensure dataset quality, compliance, and scalability. Optimize distributed data processing for training, prototype dataset tools, and integrate research advances into production systems.

Design and deploy production-grade ML systems, integrate models into software pipelines, and develop features leveraging ML for optimization and prediction problems.

Embed with enterprise customers to scope, configure, test, and run AI agent deployments from pilot to production. Translate field insights into prioritized product direction, build deployment playbooks and AI-powered tooling, coordinate across customer success, engineering, and product, and co-own measurable deployment outcomes and iterative improvements.

Contractor will design, build, and deploy internal AI-enabled web applications and tools across frontend and backend stacks. Responsibilities include UI development, APIs, databases, LLM integrations, workflow automation, code review, security best practices, and collaborating with stakeholders to translate business needs into scalable, maintainable solutions.

Drive end-to-end implementation and optimization of AI-driven customer care solutions. Gather requirements, design conversational logic and prompts, test and deploy AI features (Agent Assist, Copilot, Autopilot), monitor performance metrics, analyze transcripts, recommend improvements, support UAT and governance, and collaborate with operations, product, technology, training, analytics, and vendors to ensure successful adoption and performance.

Annotate 3D wire spans and identify visualization errors in LiDAR/camera datasets using internal tools. Maintain data integrity, document anomalies, collaborate with engineers, QA, and field techs, and deliver accurate datasets for utility infrastructure projects.

Design and ship production LLM features for a SaaS product: build customer intelligence from recordings and text, prioritize testimonials using metadata, create AI-powered surveys/interviews, iterate via research, prototyping, deployment, and evaluation. Own technical decisions and deliver measurable outcomes working closely with engineering leadership and customers.

Manage and mentor a global team of data engineers and data scientists, align technical delivery with business goals, allocate resources, improve delivery processes, drive automation and technical evangelism, and collaborate with sales and account teams to support growth.

Lead and build a US-based data science capability while remaining hands-on: write code, design, deploy, and maintain production ML models on GCP (Vertex AI), mentor junior data scientists and ML engineers, establish workflows/standards and CI/CD, collaborate with US and UK data teams and product stakeholders, and implement real-time/event-driven solutions (Kafka).