Machine Learning AI Jobs

View detail

As the Applied Data Science & Insights Lead for GTM Intelligence Solutions and Technical Success, you will be responsible for shaping how OpenAI measures, understands, and improves customer adoption across B2B products by building AI/ML-powered intelligence products that integrate various customer and product data into practical operating systems for GTM and Technical Success. You will define and lead the roadmap for GTM Intelligence and Technical Success insight products, build the data science foundation including metrics and models, develop propensity score models, and create predictive and causal models related to customer health, expansion propensity, churn risk, and intervention effectiveness. You will design next-best-action systems, partner with Technical Success leaders to enumerate playbooks and measure outcomes, develop customer segmentation and benchmarking frameworks, and create scalable insight products embedded into field workflows. Additionally, you will build and lead a small team of data scientists and analytics partners, set technical standards, create team operating rhythms, maintain analytical rigor, and collaborate with multiple departments such as Data Engineering and RevOps to improve data foundations.

View detail

The responsibilities include translating business requirements into requirements for AI/ML models, preparing data to train and evaluate AI/ML/DL models, building AI/ML/DL models using state-of-the-art algorithms especially transformers, testing and evaluating the AI/ML/DL models, publishing the models, datasets, and evaluations, deploying models in production by containerizing them, working with customers and internal employees to refine model quality, establishing continuous learning pipelines for models with online or transfer learning, and building and deploying containerized applications on cloud or on-premise environments.

View detail

As a Lead Data Scientist, you will set the technical direction for complex, business-critical projects, balancing trade-offs between speed, innovation, and reliability. You will design and implement reliable, production-grade technical solutions and ensure comprehensive documentation of architectures and specifications. You will define project problems, develop clear roadmaps, and oversee end-to-end delivery across multi-disciplinary workstreams. Your responsibilities include leading technical scoping and feasibility studies for high-value sales opportunities and strategic customer engagements, managing relationships and communications with demanding clients to align technical solutions with shared long-term commercial goals, driving the adoption of best practices, shared resources, and robust technical processes across the wider Data Science craft, and mentoring and developing other data scientists and team members to contribute to the growth and technical excellence of the organization.

View detail

Define and drive a coherent vision for leveraging data to build automation products in surveying and design, translate this vision into a technical roadmap and execute it to advance product capabilities, build and grow the machine learning team including hiring and structuring as the organization scales, mentor ML engineers and researchers by providing technical direction and career growth guidance, stay hands-on by reviewing designs, code, and architecture to maintain credibility and connection with the team, and partner with product and engineering leadership to align research investments with product strategy and customer needs.

View detail

As a Lead Data Scientist, you are responsible for setting the technical direction for complex, business-critical projects, balancing trade-offs between speed, innovation, and reliability, designing and implementing reliable, production-grade technical solutions with comprehensive documentation, defining project problems and developing clear roadmaps, overseeing end-to-end delivery across multi-disciplinary workstreams, leading technical scoping and feasibility studies for high-value sales and strategic engagements, managing relationships and communications with demanding clients to foster trust and align technical solutions with long-term commercial goals, driving the adoption of best practices and robust technical processes across the wider Data Science craft, and mentoring and developing other data scientists and team members to contribute to the growth and technical excellence of the organisation.

View detail

The Data Understanding team is responsible for creating high quality datasets and their quantized representations for OpenAI, which includes synthesizing data, building VQ representations, processing, filtering, deduplication, quality control, and tokenization to enable effective use in large model training runs. The role involves advancing how OpenAI builds and understands pretraining data at scale by treating data quality and curation as core research problems. Responsibilities include developing new methods to select, combine, and transform data, creating datasets that improve model capabilities, designing rigorous experiments to understand how data choices and interventions affect model learning and downstream behavior, and working closely with frontier models and web-scale data to build evidence for effective approaches and translate successful research into scalable data processing pipelines.

View detail

Build the eval harness for AGI covering model capability, agentic behavior, on-device performance, and end-user experience. Own eval suites gating every model and agent release, including capability, behavior, regressions, and human-rated rubrics. Maintain dashboards and tooling to facilitate fast researcher experiment loops and informed leadership decisions. Set and uphold the criteria for what counts as ready to ship. Assist research by ensuring measurements align with goals. Aid product engineers by instrumenting real-user behavior on devices. Support partnerships by translating performance improvements into measurable terms for OEM partners.

View detail

The Senior Scientist is responsible for owning the end-to-end analytical strategy for GC-MS-based programs, including method design, validation frameworks, and data quality standards for targeted and untargeted analyses. They define and evolve sample preparation methodologies for headspace, liquid-phase, and solid-phase extraction of fragrance compounds from complex matrices and consumer products. They maintain and improve Osmo's high-throughput analytical pipeline, ensuring data integrity, reproducibility, and compatibility with downstream machine learning workflows. The role involves partnering with the Platform and ML teams as the chemistry-side technical owner of the data interface, determining methods and procedures for new analytical assignments independently while coordinating execution across team members and collaborating functions. They enforce high standards of scientific rigor and data quality, mentor and develop junior and mid-level scientists, establish best practices, review work for scientific integrity, and elevate the team’s overall analytical capability. Additional responsibilities include writing, editing, and auditing analytical and experimental protocols, serving as an internal expert resource and external-facing collaborator for analytical chemistry questions across Osmo’s scientific and commercial programs.

View detail

The role involves supporting Dataiku’s strategic vision towards Healthcare & Life Sciences to accelerate growth in this industry by developing a deep understanding of Dataiku’s footprint and supporting plans to meet ambitions. Acting as a Dataiku subject matter expert (SME) on Healthcare & Life Sciences, the position requires bridging the gap between industry needs, AI, and Dataiku’s product, supporting clients and prospects as a trusted expert, and supporting development of assets and engagement support to enhance GTM effectiveness. Tasks include supporting sales and customer activities, articulating Dataiku's value proposal for Life Sciences, leveraging internal knowledge and client experiences, engaging in thought-leadership strategies, and driving high impact customer engagements. The role also entails identifying, scoping, and rolling out Dataiku Solutions aligned to Healthcare and Life Sciences needs to maximize value delivery. This includes fueling the solutions development pipeline through business experience, client workshops, market monitoring, partnership discussions, and partnering with the Solutions taskforce in ensuring prompt development of relevant projects like off-the-shelf designs, customer-specific solutions, and feature/application development. Supporting the general AI Solutions offering by contributing to documents, team enablement, customer interactions, and go-to-market materials is also required.

View detail

As a Data Scientist, you will analyze a rich array of real-world data to inform our digital twin model of the electric grid, including topography, LIDAR, imagery, vegetation, structural loading, and electrical connectivity. Your work will drive product direction with high visibility, highlight grid expansion opportunities, identify aging and risky infrastructure, and help customers understand where to build and invest. You will model accurate digital twin electric networks from imperfect data using AI, deep learning, and classical ML algorithms, surface meaningful analytics and metrics such as wildfire risk to guide customer buildout of electrical infrastructure, advise the company on data findings to inform strategy, conduct experiments and A/B tests to improve grid modeling, QA and improve predictive models while identifying data issues, craft scalable data pipelines working with various data sources including LiDAR, aerial photography, photogrammetry, and GIS, and mentor others in best practices for model training, data analytics, and building data-driven products.