Top AI & Machine Learning Jobs

Develops predictive and deep learning models for engineering and physical systems, including data preprocessing, probabilistic modeling, optimization, and scalable production data pipelines. Collaborates with simulation engineers and customers to integrate AI models into simulations, support product development, and deliver practical solutions. The role includes customer presentations, onboarding, on-site collaboration, and travel to international customer sites.

Develop and maintain scalable AI and ML architectures for a life sciences regulatory intelligence platform. Lead technical decisions, integrate LLM services and microservices, build conversational and agentic systems, manage data stores and databases, support production delivery, and collaborate in an Agile Scrum environment. The role requires hands-on Python development, generative AI deployment, cloud architecture, vector and graph databases, hybrid search, and backend API development.

Build and operate shared AI platform capabilities, APIs, SDKs, workflows, and reusable libraries for engineering teams. Develop reliable cloud-based AI and ML services using containers, orchestration, CI/CD, infrastructure as code, and strong observability. Support data preparation, deployment, monitoring, governance, security, and lifecycle management. Partner cross-functionally to productionize generative AI, retrieval, embeddings, and other machine learning use cases while improving developer enablement and operational reliability.

Conduct machine learning research operations using existing infrastructure. Characterize neural network quality, failure modes, and edge cases; coordinate experiments with researchers; manage concurrent experiments and deadlines; review results; recommend theoretical or process improvements; and prepare technical reports communicating qualitative and quantitative findings.

Build and deploy scalable machine learning models and pipelines, integrate ML into products, and develop robust systems using Python, FastAPI, React, and AWS. Lead technical design, code and architecture reviews, agile ceremonies, incident response, and reliability practices. Mentor engineers, improve testing and CI/CD, drive cross-functional alignment, and establish high standards for software quality, documentation, observability, and maintainability.

Lead the design, development, deployment, and ongoing operation of AI solutions that automate workflows and improve decision-making for internal teams and clients. Architect secure integrations with Polaris IQ, third-party tools, and client systems while partnering with strategy, product, engineering, data, and client stakeholders. Translate technical concepts into business value, manage solution lifecycles, and eventually lead a small team of AI strategists and engineers.

Own end-to-end AI product development for enterprise clients, from requirements and rapid LLM prototyping through production deployment. Responsibilities include data modeling, ETL pipelines, integrations, evaluation, observability, security, reliability, cost management, and client communication. The role requires shipping real AI products, iterating based on usage, and independently managing ambiguous technical and product decisions.

Open application for prospective team members at Seekeasy, a pre-seed startup developing generative AI recommendation engines. The company seeks builders, owners, and collaborative team players who can work autonomously, ship products, iterate from feedback, and solve real-world discovery challenges using technologies such as generative AI, computer vision, and NLP. Specific role responsibilities and qualifications are not provided.

Senior Data Scientist partnering with Product, Engineering, and Design teams to analyze user behavior, product performance, and marketplace dynamics. Responsibilities include developing metrics and reporting frameworks, designing experiments, diagnosing business trends, analyzing marketplace behavior, and aligning stakeholders on analytical priorities. The role uses SQL and Python alongside BigQuery, dbt Cloud, Airflow, Tableau, and Statsig, with a focus on product analytics, experimentation, and data-informed decision-making.

Conduct research and develop Media Mix Modeling algorithms using statistical, econometric, Bayesian, and causal inference methods. Work with time-series, panel, and marketing data; build models end-to-end; develop diagnostics and validation frameworks; run simulations; compare methodologies; and translate research into production-ready analytical solutions. Collaborate with senior data scientists on real client datasets and document methodology, assumptions, limitations, and results.