Role Description This is a rare opportunity to join one of the most strategically positioned businesses in enterprise AI security. Backed by a multibillion-dollar enterprise with direct access to the Fortune 500, this AI security platform is driving toward full market acquisition and is now building out its Forward Deployed Engineering function to own technical delivery at scale. You’ll be among the first hires shaping how the platform lands across the world’s largest organisations. The FDE sits at the centre of client delivery - embedding with enterprise accounts, building production-grade integrations, and owning the technical relationship from first deployment through to scale. This is a high ownership, high visibility role for someone who thrives in complex environments and wants to leave a mark on a rapidly growing platform. - Own end-to-end technical delivery for enterprise client engagements — from initial scoping through integration, testing, and production deployment. - Build production-grade integrations and custom tooling across complex environments - on-prem, air-gapped, and hybrid. - Diagnose and resolve integration issues across diverse enterprise stacks; be the first call when something breaks in a critical engagement. - Feed product and engineering with signal from the field - your patterns become what gets built next. Qualifications - 3–6 years in a Forward Deployed, Solutions Engineering, or technical delivery role - ideally in enterprise security, AI, or complex SaaS. - Strong Python and/or TypeScript; you write production-quality code, not just configuration scripts. - Solid grounding in REST APIs, OAuth2/SAML/SSO, and cloud platforms (AWS, GCP, or Azure) - you’ve shipped into enterprise environments and know what breaks. - The ability to hold a room with a CISO and a security engineer and be credible in both conversations. - Experience in AI security, LLMs, or agentic AI frameworks in a production context is a significant advantage. Benefits - Early stage seniority in a scaling function - the patterns you set now become the delivery standard as the team grows. - Direct influence on the product roadmap - your field observations are the earliest signal engineering gets about what to build next. - Enterprise reach, brand credibility, and a proven sales motion already in market - the infrastructure to win is already in place. - Competitive compensation benchmarked against the scarcity of this profile in the market.

Role Description As a Machine Learning Engineer in Delivery, you are a problem solver who stays anchored to impact. You are someone who can grasp advanced engineering concepts across multiple industries, and excel at working directly with customers (and often side-by-side with them on-site) to embed cutting-edge AI models into tools that are useful and used. You’ve shipped ML systems end-to-end and at scale: - Design, build and test reliable, scalable ML data pipelines. - Explore and manipulate 3D point-cloud and mesh data to enable geometry-aware modelling. - Select the right libraries, frameworks, and tools. Working at the intersection of data science and software engineering, you translate R&D and project outputs into reusable libraries, tooling, and products. With at least 2 years industry experience (post Masters or PhD) in a commercial, non-research environment, you're excited about taking ownership of complex work streams and guiding teams to success, while continuously improving the systems and solutions you work on to ensure they are practical, impactful, and meet the evolving needs of our customers. This role is remote based in the San Francisco area. What you will do - Work closely with our simulation engineers, data scientists, and customers to develop an understanding of the physics and engineering challenges we are solving. - Design, build and test data pipelines for machine learning that are reliable, scalable, and easily deployable. - Explore and manipulate 3D point cloud & mesh data. - Own the delivery of technical workstreams. - Create analytics environments and resources in the cloud or on-premise, spanning data engineering and science. - Identify the best libraries, frameworks, and tools for a given task, making product design decisions to set us up for success. - Work at the intersection of data science and software engineering to translate the results of our R&D and projects into reusable libraries, tooling, and products. - Continuously apply and improve engineering best practices and standards and coach your colleagues in their adoption. - Travel to customer sites in North America, Europe, Asia, and Oceania for an average of 3-4 weeks per quarter to collaborate closely with customers to build solutions on-site. Qualifications - Experience applying Machine learning methods (including 3D graph/point cloud deep learning methods) to real-world engineering applications, with a focus on driving measurable impact in industry settings. - Experience in ML/Computational statistics/Modelling use-cases in industrial settings (for example supply chain optimisation or manufacturing processes) is encouraged. - A track record of scoping and delivering projects in a customer-facing role. - 2+ years’ experience in a data-driven role, with exposure to software engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps). - Building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., TensorFlow, MLFlow). - Experience with distributed computing frameworks (e.g., Spark, Dask). - Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and HPC computing. - Experience with containerization and orchestration (Docker, Kubernetes). - Strong problem-solving skills and the ability to analyze issues, identify causes, and recommend solutions quickly. - Excellent collaboration and communication skills - with teams and customers alike. - A background in Physics, Engineering, or equivalent. Benefits - Build what actually matters - help shape an AI-native engineering company at a formative stage, tackling problems that genuinely matter for industry and society. - Learn alongside exceptional people - work with a high-caliber, collaborative team of engineers, scientists, and operators. - Influence over hierarchy - operate with a flat structure where good ideas win. - Equity options - share meaningfully in the company you’re helping to build. - 5% contribution to 401(k) - build long-term security with a strong retirement plan. - Private health insurance - comprehensive cover for you, offering total peace of mind. - Enhanced parental leave - 3 months full pay paternity and 6 months full pay maternity leave. - 20 days of Annual Leave (+ Public Holidays) - because taking time to rest matters. - Personal development - dedicated support for learning, development, and leveling up over time. - Gympass / Wellhub (subsidized) - for you and up to 3 family members, supporting both physical and mental wellbeing. - Flexible Spending Account (FSA) - set aside pre-tax dollars for eligible healthcare expenses. Salary range $150,000 - $190,000 depending on experience. Seniority will be assessed throughout our interview process. Company Description We value diversity and are committed to equal employment opportunity regardless of sex, race, religion, ethnicity, nationality, disability, age, sexual orientation, or gender identity. We strongly encourage individuals from groups traditionally underrepresented in tech to apply. To help make a change, we sponsor bright women from disadvantaged backgrounds through their university degrees in science and mathematics.

• Own the knowledge surface — ingestion pipelines for source code, structured metadata, technical documentation, patches, and additional corpora the customer provides. • Own retrieval quality — chunking, embeddings, hybrid retrieval, reranking, and freshness. • Own the eval harness — benchmarks for translation accuracy, dependency-map correctness, and overall agent quality. • Run A/B testing and regression detection across prompts, retrieval, and model changes. • Operate the feedback loop from production usage back into evals and retrieval. • Define what “good” means for the platform when no one else has a clear view, so the team can tell whether the agent is actually improving. • Pair with the Agent Engineers on the prompt-and-eval iteration cycle.

• Support multiple projects in various development-stage phases from proposal through kickoff, design, delivery, and revision while maintaining schedules with other disciplines and a positive relationship with clients. • Provide expert-level knowledge of solar photovoltaic (PV) electrical design including string sizing, cable sizing, ampacity, 1500V DC, 34.5kV MV, and National Electric Code (NEC) requirements for utility-scale PV power plants. • Work with internal team and external clients to develop packages including solar array layouts, electrical one-lines, collection systems, collection system studies, and equipment installation details for Utility Scale Solar PV systems (50-500MW+). • Apply current, standard engineering techniques, procedures and criteria to complete engineering tasks, and support other civil, structural, and electrical design workflows as necessary on a case by case basis. • Contribute to continual development of internal solar engineering standards, toolsets and work processes. • Develop plans, details, and reports that are complete, accurate and in accordance with sound utility-scale renewables early-stage development engineering practices. • Work closely with and provide guidance to engineering technicians and design engineers. • Partner with clients to understand priorities and technical needs, then develop feasible valued engineered solutions that achieve project goals. • Check design and computations performed by internal design team and provide ideas and execute to promote higher quality designs. • Review development plans and specifications for constructability, safety, and risk considerations. • Work closely with marketing, sales, business development, and engineering staff across multiple markets and technical disciplines. • Demonstrate a wide degree of out-of-the-box thinking, creativity, and design ownership. • Lead site visits to gather notes and take pictures of existing conditions to aid in design and identify critical flaws.