• Ship zero-to-one AI products end-to-end — from customer discovery and prototyping through production deployment and iteration. • Build agentic AI systems — design and implement autonomous and semi-autonomous workflows using LLMs, tool-use, memory, and orchestration. • Develop AI tools that improve efficiency across clinical operations, data extraction, manual workflows, and more. • Commercialize AI research — partner with the AI lab to take proprietary models from research prototypes and into production. • Contribute to model training and post-training — fine-tuning, evaluation, safety testing, and optimization of Natera's proprietary oncology models.

Role Description The graph compiler is a central piece of MAX, which executes ML models at state-of-the-art performance across multiple hardware platforms. It is built on MLIR from the ground up using a novel design based on tight integration with the Mojo programming language. Modular’s compiler stack is vertically integrated, from ML serving layer, through the Python API, down to highly tuned kernel implementations - opening up opportunities for optimization not available in competing technologies. This role involves extending and developing the graph compiler to support state-of-the-art features and hardware platforms. You will improve the performance and usability of the graph compiler, creating a joyful developer experience. Modular’s vertically integrated ML serving stack allows you to solve a uniquely diverse set of problems, like heterogeneous compilation, kernel fusions, and distributed model execution. LOCATION: Candidates based in the US or Canada are welcome to apply. You can work in our office in Los Altos, CA or remotely from home. Onboarding for new hires is conducted in-person at headquarters in Los Altos, CA. What you will do: - Design and develop compiler optimizations to improve inference efficiency across CPUs, GPUs, and ML accelerators, including kernel fusion, memory planning, and shape optimization. - Compile large-scale application workloads onto heterogeneous hardware: map hardware-agnostic graphs of computational operators onto hardware-specific graphs of devices and nodes. - Collaborate with the kernels, serving, and Mojo compiler teams to implement core technologies that deliver end-to-end performance across a variety of heterogeneous hardware platforms. - Write design documents for new features and drive for alignment on designs with cross-functional teams. - Collaborate with the customer success team and engage with customers to understand their performance requirements and use cases. Qualifications - 5+ years of software engineering experience. - Proficiency in C++. - Experience working with compilers for machine learning frameworks. - Creativity and curiosity for solving complex problems, a team-oriented attitude that enables you to work well with others. Requirements - Knowledge of and experience working with MLIR and LLVM (helpful but not required). - Experience with ML parallel / distributed programming, heterogeneous ML computation, and/or code generation (helpful but not required). - Knowledge of basic ML implementation and modeling techniques and familiarity with ML frameworks like PyTorch, JAX, or TensorFlow (helpful but not required). Benefits - Amazing Team: We are a progressive and agile team with some of the industry’s best engineering and product leaders. - World-class Benefits: Premier insurance plans, up to 5% 401k matching, flexible paid time off, and more are available to you! - Competitive Compensation: We offer very strong compensation packages, including stock options. - Team Building Events: We organize regular team onsites and local meetups in Los Altos, CA as well as different cities. Traveling 2-4 times a year is expected for all roles. Company Description Working at Modular will enable you to grow quickly as you work alongside incredibly motivated and talented people who have high standards, possess a growth mindset, and a purpose to truly change the world. The estimated base salary range for this role to be performed in the US, regardless of the state, is $198,000.00 - $286,000.00 USD. The estimated base salary range for this role to be performed in Canada, regardless of the province, is $194,000.00 - $280,000.00 CAD. The salary for the successful applicant will depend on a variety of permissible, non-discriminatory job-related factors, which include but are not limited to education, training, work experience, business needs, or market demands. This range may be modified in the future. The total compensation for a candidate will also include annual target bonus, equity, and benefits, with equity making up a significant portion of your total compensation. For candidates who fall outside of the listed requirements, we nevertheless encourage you to apply as we may have openings that are lower/higher level than the ones advertised.

Senior AI Engineer remote type Fully Remote locations USA - IL (Remote) time type Full time job requisition id R29808 At Allstate, great things happen when our people work together to protect families and their belongings from life’s uncertainties. And for more than 90 years, our innovative drive has kept us a step ahead of our customers’ evolving needs. From advocating for seat belts, air bags and graduated driving laws, to being an industry leader in pricing sophistication, telematics, and, more recently, device and identity protection. Job Description We are seeking a Senior AI Engineer on the Enterprise Intelligence Factory team to play a foundational role in building the enterprise Semantic Ontology & Dimension Factory Platform. This platform enables AI‑ready analytics by combining semantic ontologies, knowledge graphs, agentic AI, and data engineering to automatically generate business‑ready star schemas from heterogeneous enterprise data sources. In this role, you will design and implement agent‑driven pipelines that leverage RDF/OWL ontologies, SPARQL, and Large Language Models (LLMs) to perform semantic alignment, dimension mining, and AI‑assisted data modelling at scale. You will work at the intersection of AI, semantics, and modern data platforms, helping establish engineering patterns and best practices for the team. This is a hands‑on, senior individual-contributor role, ideal for engineers who enjoy building core platform capabilities rather than isolated experiments. Key Responsibilities: - Design, build, and maintain agentic AI pipelines (using Google ADK or similar frameworks) to automate semantic mapping, dimension mining, and ontology‑driven reasoning. - Create and evolve enterprise ontologies in RDF/OWL, including upper ontologies and domain extensions aligned to CIM (where applicable), to enable reusable enterprise semantics. - Engineer LLM‑powered services for schema understanding, semantic alignment, ontology enrichment, and AI‑assisted metadata generation, with a focus on accuracy, traceability, and scale. - Implement SPARQL querying and reasoning layers over knowledge graphs to drive downstream transformations and ensure consistent interpretation of business concepts. - Architect and deliver Python‑based microservices and batch pipelines that integrate semantic reasoning with modern data‑engineering workflows. - Build and optimize dimension and fact generation pipelines on Microsoft Fabric (Lakehouse, Spark, SQL, orchestration) to produce business‑ready star schemas from heterogeneous sources. - Define and enforce engineering standards, design patterns, and reusable components for semantic and AI‑driven data platforms (quality, observability, security, and performance). - Partner with data architects, domain SMEs, and governance teams to validate semantic definitions, manage change, and ensure platform scalability and adoption. - Conduct code reviews, mentor engineers, and influence technical decisions across the platform to raise engineering quality and delivery velocity. Required Skills & Qualifications: - 6+ years of professional software engineering experience, with strong proficiency in Python and GenAI. - Hands‑on experience building LLM‑based systems using commercial or open‑source models. - Solid understanding of semantic technologies: RDF, OWL, ontologies, knowledge graphs, and SPARQL. - Experience designing or working with agentic AI frameworks (e.g., Google ADK, LangChain agents, or similar). - Strong background in data engineering concepts (ETL/ELT, star schemas, metadata‑driven pipelines). - Experience building and operating systems on cloud platforms, preferably Microsoft Azure / Microsoft Fabric. - Strong problem‑solving skills and ability to work in ambiguous, greenfield platform initiatives. Preferred Skills: - Experience with enterprise data models (e.g., CIM or canonical models). - Familiarity with semantic alignment, ontology mapping, or data cataloguing tools. - Exposure to MLOps / LLMOps, model evaluation, and AI observability. - Knowledge of distributed systems, CI/CD pipelines, and containerisation. - Experience building AI‑assisted analytics or semantic layers for BI or NLQ use cases. The hiring manager has flexibility to hire across several levels of seniority. The level will be determined by the selected applicant's skills and competencies. #LI-TE1 Skills Agentic AI, Agentic Design, AI Frameworks, Large Language Models (LLMs), LLM Guardrails, LLM Orchestration, Ontology, Python (Programming Language) Compensation Compensation offered for this role is 100,000.00 - 170,500.00 annually and is based on experience and qualifications. The candidate(s) offered this position will be required to submit to a background investigation. Joining our team isn’t just a job — it’s an opportunity. One that takes your skills and pushes them to the next level. One that encourages you to challenge the status quo. One where you can shape the future of protection while supporting causes that mean the most to you. Joining our team means being part of something bigger – a winning team making a meaningful impact.

• Build and maintain scalable data pipelines using Databricks and Azure Data Lake Storage Gen2 (ADLS Gen2), ensuring reliability and performance across all pipeline stages. • Implement the Medallion Architecture (Raw → Silver → Gold layers) to clean, normalise, and structure technical documentation for downstream AI consumption. • Design and develop the Reasoning Layer using frameworks such as LangChain or LlamaIndex to manage LLM logic, prompt routing, and tool orchestration. • Manage and tune Vector Databases (e.g. Pinecone, Weaviate, or Azure AI Search) to enable fast and accurate semantic retrieval across the corporate knowledge base. • Develop Azure Apps and Azure Functions to connect the AI system to external enterprise platforms such as Salesforce and SharePoint, enabling end-to-end automation.