Fundamental

• Develop and manage scalable, automated machine learning pipelines, CI/CD workflows, and orchestration frameworks • Design and implement robust model serving infrastructure using platforms like TorchServe, TensorFlow, Triton etc. • Develop scalable inference architectures optimized, with ultra-low latency and high throughput • Ensure seamless model deployment by implementing A/B testing, canary releases, and rollback capabilities • Develop logging, alerting, and monitoring solutions to track model development, and reliability • Improve GPU usage, enable autoscaling, and streamline resource allocation to boost efficiency • Design, implement, and maintain feature stores, robust data pipelines, and scalable storage solutions to efficiently handle large volumes of data

• Lead and mentor a team of MLOps engineers, fostering technical growth and a culture of operational excellence • Define and drive the MLOps roadmap, aligning infrastructure capabilities with Research, Engineering and product objectives • Establish best practices, standards, and processes for ML infrastructure, deployment, and operations • Own technical decision-making for ML infrastructure architecture and tooling choices • Architect and oversee scalable, automated machine learning pipelines, CI/CD workflows, and orchestration frameworks • Drive the design and implementation of robust model serving infrastructure using platforms like Triton, TorchServe, TensorFlow Serving, and KServe • Define inference architecture strategy optimized for ultra-low latency and high throughput • Design and maintain feature stores, robust data pipelines, and scalable storage solutions to efficiently handle large volumes of data • Collaborate with research teams to bridge the gap between experimentation and production • Define logging, alerting, and monitoring strategy to track model performance, drift, and system reliability

• Research and develop data science methods that improve NEXUS predictive performance across diverse enterprise datasets, industries, and prediction task types • Design and implement robust, production-quality Python components with a strong focus on correctness, generality, and reusability • Deeply understand the characteristics of real-world enterprise data and develop strategies that help NEXUS handle them reliably • Run rigorous experiments to measure the impact of new approaches, design meaningful benchmarks, and use results to guide prioritization • Work across a wide variety of structured data problems - including but not limited to classification, regression, ranking, and forecasting • Collaborate closely with the Engineering and Research teams to develop a deep understanding of NEXUS model behavior and use that knowledge to inform your work • Work with Applied AI Engineers to validate approaches on real customer datasets and translate findings into product capabilities • Contribute to technical documentation and internal best practices, helping the broader team apply new capabilities correctly and confidently

• Own the reliability, performance, and scalability of Extensions capabilities end to end • Design and build distributed workflows that are efficient, reproducible, and observable in production • Collaborate closely with Data Scientists on the team to deeply understand requirements and translate them into engineering decisions • Identify and resolve performance bottlenecks, ensuring Extensions capabilities meet the latency and throughput demands of enterprise deployments • Contribute to architectural decisions around how Extensions integrates with the broader NEXUS backend • Write clean, well-tested Python code and hold a high bar for code quality across the team • Contribute to internal engineering documentation and help establish best practices for backend development within the team

• Take part in development and optimization of a large neural network-based tabular model implemented in Python • Profile training and inference pipelines to identify performance bottlenecks • Rewrite critical components in Rust (via PyO3 or custom extensions) where Python limits us, with C++ (via PyBind11 or custom extensions) as a secondary option where appropriate • Improve memory efficiency, latency, and throughput across model pipelines • Ensure correctness, numerical stability, and reproducibility as the model evolves • Collaborate with ML researchers on productionizing new capabilities • Maintain clean abstractions, comprehensive tests, and clear documentation • Shape architectural decisions for our ML systems handling tabular data

• Manage and grow the social presence of Fundamental and its executive leadership team across LinkedIn and X • Develop thoughtful, high-quality content that resonates with enterprise technology audiences, data science leaders, executives, and industry operators • Help shape the voice, positioning, and online presence of company leadership • Create content strategies aligned with major company moments including conferences, launches, customer stories, research, partnerships, campaigns, and industry trends • Work closely with leadership to develop authentic, opinion-driven content and perspectives on the future of AI, enterprise technology, and prediction • Identify opportunities for timely commentary, reactive content, and strategic participation in industry conversations • Collaborate with events, design, content, and marketing teams to turn company activities into engaging digital narratives • Help produce content around conferences, executive dinners, customer events, podcasts, launches, campaigns, and other brand moments throughout the year • Monitor engagement quality, audience relevance, and strategic impact — not just top-line impressions or follower growth • Stay highly plugged into conversations happening across AI, enterprise software, startups, technology, and adjacent industries • Experiment with new formats, creative approaches, and storytelling styles that differentiate Fundamental from traditional enterprise brands

• Manage calendars and travel arrangements for the CEO and leadership team, optimizing time and anticipating needs. • Coordinate internal and external meetings, including investor calls, team offsites and client sessions. • Prepare agendas and track follow-ups for key meetings, ensuring tight execution and accountability. • Help operationalize internal processes — onboarding, documentation, knowledge sharing — to support team velocity. • Own day-to-day admin tasks (e.g receipts), while constantly improving systems and workflows.

About Fundamental Fundamental is an AI company pioneering the future of enterprise decision-making. Founded by DeepMind alumni, Fundamental has developed NEXUS – the world's most powerful Large Tabular Model (LTM) – purpose-built for the structured records that actually drive enterprise decisions. Backed by world class investors and trusted by Fortune 100 companies, Fundamental unlocks trillions of dollars of value by giving businesses the Power to Predict. At Fundamental, you'll work on unprecedented technical challenges in foundation model development and build technology that transforms how the world's largest companies make decisions. This is your opportunity to be part of a category-defining company from the ground-up. Join the team defining the future of enterprise AI. About the role Fundamental is seeking a Forward deployed Data Scientist to facilitate the adoption of NEXUS and collaborate with customers to address complex technical challenges. The Data Scientist is an integral part of our FDE team, which is dedicated to driving the successful deployment of Fundamental products and proving value over legacy baselines or net new use cases. They work hand-in-hand with customers from the Proof of Value stage to post-implementation, ensuring our solutions run securely in the client's production heartbeat. In this role, you’ll manage customer relations involving multiple stakeholders (IT, C-suite, and data science teams) and function as a key bridge, translating field insights into our product roadmap. Key responsibilities - You’ll individually help deploy into production use cases with considerable business impact, moving from "science experiments" to definitive ROI - You’ll work on rigorous head-to-head benchmarking against client baselines (XGBoost, LightGBM), executing the work of data engineering, feature engineering, and validation - You’ll work in collaboration with our research and product teams to translate operational pain points and data anomalies into essential inputs for the Fundamental roadmap - You’ll be involved in technical strategy to identify the right business problems, prevent data leakage, and handle the "last mile" integration (VPC, on-prem, air-gapped) - Your collaboration with the Sales and Solution Architect teams will help align diverse stakeholders and explain predictions to business users Must have - You hold a PhD / master in CS / Math / Stats or you have equivalent deep statistical literacy - You have 2+ years as a technical individual contributor (data scientist or software engineer) - You have experience with containerization (Docker), orchestration, and writing performant APIs (FastAPI/Flask) - You master the end-to-end pipeline, from framing, pre-processing, ml algorithm and validation strategies - You have a deep understanding of data handling (PySpark, Pandas) and memory optimization - You have demonstrated experience optimizing models for a specific business problem - You hold strong communication skills with an ability to translate architectural nuances into clear business value Nice to have - Experience with PyTorch and cloud-native ML pipelines (AWS, GCP, Azure) - Experience as a Forward Deployed Engineer, Staff Engineer, Machine Learning Engineer, or Staff Data Scientist - Industry-based subject matter expertise Benefits - Competitive compensation with salary and equity - Comprehensive health coverage, including medical, dental, vision, and 401K - Paid parental leave for all new parents, inclusive of adoptive and surrogate journeys - Relocation support for employees moving to join the team in one of our office locations - A mission-driven, low-ego culture that values diversity of thought, ownership, and bias toward action

• Design, build, and maintain production model serving infrastructure using Triton Inference Server as the primary framework • Implement and optimize inference pipelines including custom backends, dynamic batching strategies, and model ensemble configurations in Triton • Optimize Python inference code for performance, with a strong focus on GIL contention, multi-threading, and concurrency patterns • Tune throughput and latency across the full serving stack, batching policies, thread pool sizing, model instance groups, and memory layout • Work closely with the research team to understand new model architectures at a computational level, batching behavior, dynamic shapes, memory access patterns etc • Own the full resource observability and control loop for production inference - instrument GPU memory, CPU, batch queue depth, and latency metrics, and actively tune model instance groups, concurrency limits, memory budgets, and batching configuration in response to observed behavior • Evaluate and integrate alternative inference frameworks and runtimes as the model ecosystem evolves • Contribute to GPU utilization improvements and resource efficiency across the serving fleet

• Own the lifecycle of research programs, from scoping and roadmapping to execution and post-launch • Work with the research team to turn complex research objectives into actionable plans • Implement “just enough” process. Facilitate stand-ups, retrospectives, and anything else required to keep the team moving fast without the drag of bureaucracy • Track dependencies across the team - from data requirements to compute resource allocation • As an early member of the team, you will help define our internal tooling, documentation standards, and how we collaborate as we scale