Fundamental

About Fundamental Fundamental is an AI research lab pioneering the future of enterprise decision-making. Our flagship model, NEXUS is the world's most powerful Large Tabular Model (LTM) - purpose-built for the structured records that contain trillions of dollars in business value. With $275m in funding from leading investors and trusted by Fortune 100 companies, Fundamental is 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. Key responsibilities • 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 • Partner with the model-serving team on serving infrastructure strategy (Triton, TorchServe, TensorFlow Serving, KServe), ensuring training-side decisions (checkpoint formats, export pipelines, resource footprint) don't create friction downstream • Collaborate on inference architecture strategy, bringing training-side context on model size, latency/throughput tradeoffs, and hardware requirements into early research decisions • 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 Must have • Bachelor's or Master's degree in Computer Science, Engineering, or a related field (or equivalent practical experience) • 7+ years of experience in MLOps, with 5+ years in a technical leadership role • Strong software engineering skills in Python, with experience in Bash and/or Go • Proven track record of building and leading high-performing MLOps or infrastructure teams • Experience building and designing MLOps infrastructure from the ground up • Deep experience with MLOps platforms (MLflow, WandB, etc.) and frameworks (PyTorch, TensorFlow, etc.) • Deep experience with model serving frameworks (Triton, TorchServe, TensorFlow Serving, KServe) for high scalability and low latency inference • Experience building and managing data pipelines to support both model training and inference • Good experience with Kubernetes on a major cloud provider (AWS, GCP, or Azure) and with infrastructure as code (Terraform, Helm, GitOps) • Proficient with observability and monitoring tools (Prometheus, Grafana, Datadog, OpenTelemetry) • Excellent communication skills with ability to translate between research and production contexts Nice to have • Experience with workflow orchestration tools (Kubeflow, Airflow, Argo Workflows) • Experience with FastAPI and backend applications • Familiarity with data platforms like Databricks or Snowflake • Experience with LLM/foundation model serving and optimization • Exposure to SRE practices or cloud security certifications • Experience scaling ML infrastructure for AI startups Benefits • Competitive compensation with salary and equity • Comprehensive health coverage for you and your dependents • 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

About Fundamental Fundamental is an AI research lab pioneering the future of enterprise decision-making. Our flagship model, NEXUS is the world's most powerful Large Tabular Model (LTM) - purpose-built for the structured records that contain trillions of dollars in business value. With $275m in funding from leading investors and trusted by Fortune 100 companies, Fundamental is 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. Key responsibilities • 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 Must have • Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent practical experience) • 5+ years of experience as MLOps engineer or DevOps roles, working with MLOps platforms (MLflow, WandB etc..) and frameworks (PyTorch, TensorFlow etc..) • Experience building and designing MLOps infrastructure from the ground up • Experience with model serving frameworks (TorchServe, TensorFlow Serving, Triton, KServe etc..) for high scalability and low latency inference • Experience in building and managing data pipelines to support both model training and inference • Experience with Kubernetes on a major cloud provider (AWS, GCP, or Azure) and with infrastructure as code (e.g. Terraform, Helm, GitOps) • Strong software engineering skills in Python, Bash, and Go, with a focus on writing clean, maintainable, and scalable code • Experience in AI/ML systems security, compliance, and model governance • Proficient with observability and monitoring tools, such as Prometheus, Grafana, Datadog, and OpenTelemetry Nice to have • Experience with ML workflow tooling (MLflow, Kubeflow, or similar) • Experience with FastAPI and Backend applications • Familiarity with data platforms like Databricks or Snowflake • Exposure to SRE practices or cloud security certifications • Hands-on experience with Prometheus, Grafana, or Datadog Benefits • Competitive compensation with salary and equity • Comprehensive health coverage for you and your dependents • 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

About Fundamental Fundamental is an AI research lab pioneering the future of enterprise decision-making. Our flagship model, NEXUS is the world's most powerful Large Tabular Model (LTM) - purpose-built for the structured records that contain trillions of dollars in business value. With $275m in funding from leading investors and trusted by Fortune 100 companies, Fundamental is 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 In this role, you'll research, develop, and productize data science capabilities that enhance and expand our product performance on real enterprise use cases - working across a wide range of prediction tasks, data types, and business domains. You'll go deep on hard data science problems, collaborate closely with R&D on product capabilities, and ship production-grade work that has a direct impact on Production use cases. Key responsibilities • 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 Must have • 5+ years of experience in data science or machine learning roles • Strong Python skills, including fluency with pandas, numpy, and scikit-learn • Deep hands-on experience with traditional ML models: XGBoost, LightGBM, CatBoost, and similar gradient boosting frameworks • Solid understanding of what makes real-world tabular data challenging: class imbalance, high cardinality, distribution shift, missing values, and more • Strong experimental mindset - comfortable designing benchmarks and drawing rigorous conclusions from noisy results • Ability to work autonomously and drive work from idea to shipped output Nice to have • Familiarity with tabular foundation models (TabPFN, CARTE, or similar) • Competitive data science experience (Kaggle, DrivenData, or similar) - especially top finishes on tabular competitions • Background in a domain where structured prediction matters: finance, supply chain, healthcare, retail, or industrial • Experience contributing to or designing internal ML libraries or shared tooling • Familiarity with DuckDB, Polars, or modern in-process analytics engines • Comfort reading ML research papers and translating findings into practical implementations Benefits • Competitive compensation with salary and equity • Comprehensive health coverage for you and your dependents • 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

About Fundamental Fundamental is an AI research lab pioneering the future of enterprise decision-making. Our flagship model, NEXUS is the world's most powerful Large Tabular Model (LTM) - purpose-built for the structured records that contain trillions of dollars in business value. With $275m in funding from leading investors and trusted by Fortune 100 companies, Fundamental is 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. Key responsibilities • Lead and mentor a team of DevOps engineers, fostering technical growth and collaboration • Define and drive the infrastructure roadmap aligned with company objectives • Architect and oversee cloud infrastructure design and implementation • Establish best practices, standards, and processes for infrastructure development and operations • Partner with Engineering, Research, and FDE to align infrastructure capabilities with business needs • Drive the evolution of Kubernetes clusters optimized for GPU workloads, Production SaaS hosting and and varied enterprise deployment models • Champion GitOps practices using ArgoCD for continuous deployment • Establish infrastructure as code standards using Terraform • Define monitoring and observability strategy for distributed systems • Collaborate with ML engineers to optimize infrastructure for model training and serving • Own infrastructure reliability, performance, and security posture • Implement and maintain cost optimization strategies (FinOps) for cloud resources Must have • 7+ years of experience in cloud infrastructure and DevOps, with 5+ years in a technical leadership role • Proven track record of building and leading high-performing infrastructure teams • Strong experience with AWS, GCP and Azure • Deep expertise in Kubernetes, including multi-cluster management, GPU workload optimization, resource scheduling and autoscaling, and network policies and security • Extensive experience with cloud networking, including VPC design, load balancer configuration, network security and segmentation, and cross-cloud networking solutions • Strong CI/CD expertise, preferably with GitHub Actions • Proficiency in Terraform • Proficiency with GitOps tools (ArgoCD preferred) • 3+ years of experience with Python • Experience with monitoring and observability tools • Experience with FinOps practices and cloud cost optimization • Excellent communication skills with ability to translate technical concepts for diverse audiences Nice to have • Experience with ML workflow tooling (MLflow, Kubeflow, or similar) • Experience with FastAPI and backend applications • Familiarity with data platforms like Databricks or Snowflake • SRE practices experience or cloud security certifications • Hands-on experience with Prometheus, Grafana, or Datadog • Experience scaling infrastructure for AI/ML startups Benefits • Competitive compensation with salary and equity • Comprehensive health coverage for you and your dependents • 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

About Fundamental Fundamental is an AI research lab pioneering the future of enterprise decision-making. Our flagship model, NEXUS is the world's most powerful Large Tabular Model (LTM) - purpose-built for the structured records that contain trillions of dollars in business value. With $275m in funding from leading investors and trusted by Fortune 100 companies, Fundamental is 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. Key responsibilities • 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 Must have • Strong software engineering fundamentals with expert-level Python and Rust • Hands-on experience bridging Python and Rust (PyO3, maturin, or custom extensions) • Experience developing and maintaining ML models in production • Strong understanding of neural networks • Track record of optimizing performance-critical code • Strong profiling and debugging skills (CPU, memory, latency) Nice to have • Experience with tabular ML approaches (transformers, tree/NN hybrids, learned embeddings) • Working proficiency in C++ and experience bridging Python and C++ (PyBind11, Cython, or custom extensions) • Familiarity with PyTorch internals or writing custom ops (Rust or C++) • Experience optimizing training loops, data pipelines, or inference engines • Background in numerical computing or systems programming • Exposure to large-scale ML infrastructure (distributed training, batching, caching) • Experience with the Rust async ecosystem (tokio) or SIMD/parallelism crates (rayon, ndarray) Benefits • Competitive compensation with salary and equity • Comprehensive health coverage for you and your dependents • 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

About Fundamental Fundamental is an AI research lab pioneering the future of enterprise decision-making. Our flagship model, NEXUS is the world's most powerful Large Tabular Model (LTM) - purpose-built for the structured records that contain trillions of dollars in business value. With $275m in funding from leading investors and trusted by Fortune 100 companies, Fundamental is 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. Key responsibilities As part of the Research team, you will contribute to ideation, designing, implementing, and evaluating breakthrough Machine Learning models that will be deployed in the real world. Your work will be focused on the entire lifecycle of the models. Alongside the rest of the ML researchers in the team, you will be responsible for our models’ performance in every meaning of this word - whether this means achieving high evaluation scores through novel architectures and training methods, establishing the evaluation protocols themselves, or implementing methods that allow for efficient training and inference. The greatest research is done through solid engineering, so alongside the research you will also take part in ensuring that our research code allows swift, rapid development and testing of new ideas - both your own and the rest of the team’s. Must have • Strong familiarity with the full research cycle in Machine Learning • Strong fundamentals of software engineering • Strong knowledge of Python, and its ML frameworks • Experience with: • Full lifecycle of AI model development • ML infrastructure frameworks and tools • Developing new ML methods, algorithms and models • GPUs (or TPUs) and distributed training • Scaling up models and training regimes • Knowledge of: • Classical ML methods and algorithms • Deep Learning techniques • Expertise in at least one of the following: • Tabular models, time series models, relational foundation models, structured data, large-scale foundation models Nice to have • Published research at AI conferences • Contributions to open source ML projects • Experience working with tabular data / predictive analytics • High Kaggle rank • BSc/MSc/PhD in computer science/machine learning Benefits • Competitive compensation with salary and equity • Comprehensive health coverage for you and your dependents • 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

About Fundamental Fundamental is an AI research lab pioneering the future of enterprise decision-making. Our flagship model, NEXUS is the world's most powerful Large Tabular Model (LTM) - purpose-built for the structured records that contain trillions of dollars in business value. With $275m in funding from leading investors and trusted by Fortune 100 companies, Fundamental is 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 In this role, you'll develop, and productize data science capabilities that enhance and expand our product performance on real enterprise use cases - working across a wide range of prediction tasks, data types, and business domains. You'll go deep on hard data science problems, collaborate closely with R&D on product capabilities, and ship production-grade work that has a direct impact on Production use cases. Key responsibilities • 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 Must have • 5+ years of backend engineering experience, with a strong focus on Python • Excellent Python skills - you write clean, idiomatic, well-tested code and know the language deeply • Solid experience designing and operating distributed systems at production scale • Strong understanding of concurrency, task execution, and workflow orchestration patterns • Experience building systems that are reproducible, observable, and easy to debug in production • Ability to work closely with non-engineering counterparts and translate complex requirements into reliable implementations • Strong ownership mindset - you drive work end to end and care about outcomes, not just output Nice to have • Hands-on experience with workflow orchestration systems such as Temporal, Dramatiq, Celery, or equivalent • Familiarity with ML infrastructure or experience working alongside data science teams • Experience with high-throughput data processing pipelines • Exposure to Kubernetes and container-based deployment environments • Background in performance profiling and optimization of Python services Benefits • Competitive compensation with salary and equity • Comprehensive health coverage for you and your dependents • 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

About Fundamental Fundamental is an AI research lab pioneering the future of enterprise decision-making. Our flagship model, NEXUS is the world's most powerful Large Tabular Model (LTM) - purpose-built for the structured records that contain trillions of dollars in business value. With $275m in funding from leading investors and trusted by Fortune 100 companies, Fundamental is 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 NEXUS is already in production, and we are working to substantially improve its predictive quality, latency, and cost efficiency. You will lead the team that owns how NEXUS is measured, building the shared evaluation platform that engineering, research, and Applied AI all rely on: consistent benchmarks, curated datasets, and standards for metrics, data splits, leakage prevention, and benchmark contamination that hold up under scrutiny. Research needs to trust results before committing compute to it, engineering needs regressions caught before they reach production, and when a customer's own data science team benchmarks NEXUS against their own models, your platform is what Applied AI relies upon. You will benchmark NEXUS against competing approaches, using fair tuning budgets, data access, and latency measurement protocols, and turn what you find into research priorities and release recommendations. This is a player-coach role: you will hire and manage a small team while staying hands-on with the code, the experiment design, and the methodology yourself. There is no evaluation function to inherit here - what you build becomes the standard the rest of the company measures NEXUS against. Key responsibilities • Build a shared evaluation platform for engineering, research, and Applied AI. Continuously add and maintain models and curated datasets so teams can run benchmarks and investigate results independently. • Define evaluation standards for metrics, data splits, leakage prevention, calibration, uncertainty, and benchmark contamination. • Build reproducible pipelines with versioned inputs and artifacts, and integrate regression checks into research and release workflows. • Benchmark NEXUS against competing approaches using fair tuning budgets, data access, compute, and latency measurement protocols. • Support Applied AI’s customer POC evaluations with tooling, methodological guidance, and analysis. • Measure predictive quality, latency, and cost across deployment configurations, task types, and dataset characteristics. • Turn findings into research priorities, release recommendations, and evidence-backed customer improvement plans. • Hire and develop the team, set priorities, and stay hands-on with code and experimental design. About the role • Experience owning evaluation for tabular ML systems used in production or consequential customer decisions. • Strong statistical judgment: choosing metrics and validation schemes, estimating uncertainty, comparing models across datasets, and accounting for repeated experimentation. • Practical experience finding leakage in preprocessing, feature construction, joins, temporal dependencies, and related entities across splits. • Strong Python and SQL skills, familiarity with scikit-learn and gradient-boosted trees, and experience building reliable ML tooling or platforms used by other teams. • Experience designing fair model comparisons, including hyperparameter search, resource budgets, and end-to-end latency measurement. • Prior people management experience, including hiring, technical coaching, and performance feedback, while remaining technically involved. • Clear written and spoken communication with researchers, engineers, and customer data scientists, including the willingness to challenge claims the evidence does not support. Nice to have • Experience evaluating tabular foundation models or AutoML systems. • Experience measuring how model optimisations affect predictive quality and inference performance. • Experience with relational or multi-table data, and Snowflake or Databricks environments. • Experience evaluating automated or agent-driven ML workflows, including failures that aggregate metrics can hide. Benefits • Competitive compensation with salary and equity • Comprehensive health coverage for you and your dependents • 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