Jobs at Invisible Technologies

Lead the technical strategy and multi-year roadmap for Data Labs, overseeing AI benchmarking systems, data pipelines, and reinforcement-learning environments. Manage engineering managers, forward-deployed engineers, and senior individual contributors while partnering with research, product, commercial, and executive teams. Guide platform investment, evaluation methodology, market positioning, client engagement, organizational growth, and architectural decisions supporting AI training and deployment.

Own revenue growth across foundation AI labs, technology companies, and enterprises. The role leads account expansion, new-logo sales, multi-million-dollar negotiations, forecasting, and executive relationships while shaping product packaging, pricing, and new revenue lines. It also partners with Product, Engineering, Delivery, and Operations to commercialize AI data, evaluation, staffing, and agentic offerings, build repeatable sales motions, lead a growing commercial team, and represent the company in the frontier AI community.

Build and deploy end-to-end statistical and machine learning solutions for clients and internal delivery teams. Responsibilities include scoping ambiguous problems, analyzing operational challenges, prototyping AI workflows, translating messy data and real-world constraints into scalable solutions, iterating with stakeholders, and developing reusable tooling and infrastructure. The role combines hands-on data science with technical consulting and requires communication with technical and non-technical stakeholders.

Lead end-to-end data science and machine learning projects, from scoping ambiguous client problems through production deployment. Build and evaluate statistical and ML models, analyze large datasets, run experiments, collaborate with engineers, create reusable tooling, communicate findings to diverse stakeholders, and mentor data scientists. The role combines hands-on data science with technical consulting and requires occasional customer on-call support.

Generalist People Experience Manager responsible for creating HR policies, competency frameworks, communications, and manager resources; managing People team programs; handling US employee relations cases; and improving People Operations processes. The role coordinates cross-functional stakeholders, supports HRIS and compliance projects, analyzes workforce data, and operates effectively in a fast-growing, distributed organization.

Leads high-volume recruiting delivery for client staffing programs, owning workforce forecasting, sourcing, hiring, onboarding readiness, backfills, SLAs, and recruiting metrics. Manages recruiters, sourcers, coordinators, and contractors while serving as the primary client talent partner. Builds scalable hiring processes, manages sourcing channels and budgets, ensures employment compliance, analyzes ATS funnel data, and supports staffing proposals and RFPs.

Build and deploy AI-powered solutions for clients by scoping ambiguous problems, designing LLM workflows, integrating data and retrieval systems, and creating scalable production tooling. Collaborate with technical and non-technical stakeholders, iterate based on operator feedback, optimize workflows, and support customer deployments. The role requires strong Python and software or ML engineering skills, production deployment experience, communication abilities, customer on-call availability, travel as needed, and hybrid office attendance.

Build and maintain evergreen technical talent pipelines for data engineering, machine learning, and full-stack roles. Source and engage candidates, coordinate with sales, delivery, engineering, and hiring leaders, define technical assessments, manage full-cycle recruiting, optimize sourcing and conversion metrics, and maintain candidate readiness for rapid deployment. This is a 3–6 month contract engagement at approximately 40 hours per week.

Design scalable human-data solutions for frontier AI clients, translating ambiguous model-training and evaluation needs into executable programs. Lead discovery, methodology, task and workflow design, feasibility estimates, expert profiling, quality strategies, and technical architecture in partnership with engineering and project teams. Advise on fine-tuning, RLHF, benchmarks, red teaming, multimodal data, coding tasks, and agent environments while guiding pilots through early delivery and developing reusable best practices.

Build and deploy AI-powered solutions directly with clients and internal delivery teams. Responsibilities include scoping ambiguous problems, designing LLM and retrieval workflows, developing scalable backend and infrastructure systems, optimizing operational workflows, translating messy data into technical architectures, iterating with stakeholders, and creating reusable deployment tooling. The role also involves client communication, end-to-end project ownership, customer on-call support, travel for client engagements, and hybrid office work.

Designs benchmarks, reinforcement learning environments, evaluation methodologies, scoring frameworks, and reproducible evaluation architectures for frontier models. Builds and ships production code, maintains data pipelines, analyzes results, and collaborates with research scientists, solutions architects, and ML engineers. The role combines independent research methodology with hands-on engineering across agentic systems, model evaluation, and RL infrastructure.

Own strategic planning, workflow automation, compliance operations, systems support, data monitoring, documentation, vendor communications, and cross-functional coordination for a Legal and Compliance team. Translate priorities into roadmaps, improve recurring processes, troubleshoot business systems and data issues, and guide application requirements and QA while maintaining accuracy in a high-pressure environment.

Build and deploy production-grade agentic AI and machine learning systems, including scalable backend services, data pipelines, RAG applications, and cloud infrastructure. Collaborate with ML engineers, data scientists, and clients to discover use cases, develop AI-powered products, optimize platform performance, and support deployments. The role also involves MLOps, model monitoring and versioning, automated testing, infrastructure management, research and development, and direct client engagement.

Own discovery, scoping, delivery, and stakeholder management for AI-enabled data products serving enterprise financial-services clients. Lead workshops, define success metrics, manage timelines and OKRs, coordinate engineering teams, design automation and human-in-the-loop solutions, and maintain executive relationships. The role also requires data analysis, governance, quality, metadata, lineage, playbook development, mentoring, and collaboration with product and sales teams.

Build and deploy AI-powered solutions for client operational problems. Responsibilities include scoping ambiguous requirements, designing LLM and retrieval workflows, developing scalable software, working with messy data, iterating with clients and operators, and creating reusable deployment infrastructure. The role combines AI engineering, software development, and technical consulting, with customer-facing collaboration, on-call responsibilities, and 25–50% travel for client engagements.

Respond to P1/P2 production incidents in a 24/7 rotation, triage infrastructure failures using Kubernetes, RabbitMQ, and PostgreSQL logs, stabilize systems, implement infrastructure fixes, escalate application issues, communicate incident status, and support system hardening and scaling improvements at senior levels.

Own a platform domain end to end, defining architecture, roadmap, reliability, security, and cost standards. Build scalable multi-tenant Kubernetes infrastructure, enable self-service deployments, advance agent-native engineering practices, and develop tooling for safe agent execution. Partner with engineering teams to translate infrastructure needs into reusable capabilities, mentor engineers, lead design decisions, and manage on-call and post-incident processes.

Leads operational delivery for complex enterprise AI engagements, aligning commercial, engineering, and executive client stakeholders. Owns multi-workstream execution, agile delivery ceremonies, requirements scoping, risk mitigation, KPI and ROI analysis, process design, and business cases. Partners with technical leads on strategic deployments, structures ambiguous challenges into scalable frameworks, advises senior clients, and mentors Enterprise Solutions Managers and delivery teams.

Leads enterprise client engagement, solution design, operational process creation, cross-functional project delivery, workflow optimization, team reviews, staff mentoring, performance analysis, and real-time analytics initiatives. The role bridges enterprise clients, global talent, and internal product, engineering, and operations teams while building scalable processes and supporting the launch of a next-generation marketplace.

Work directly with clients to scope, prototype, and deploy AI-powered workflows. Build scalable systems using LLMs, embedding and retrieval systems, and backend infrastructure. Translate messy data and operational constraints into production-ready solutions, iterate with stakeholders, and maintain deployed systems (including on-call support).