Jobs at Invisible Technologies

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.

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.

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.

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.

The Enterprise Solutions Manager drives operational excellence and client success through strategic analysis, process optimization, and project management, collaborating with various teams to deliver AI solutions.

Manage relationships with strategic healthcare clients, oversee complex projects, innovate operational processes, and lead large teams to improve AI training and application.

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).

Lead end-to-end AI solutions as a forward-deployed engineer: scope, design, and deploy ML/LLM systems; partner with clients and stakeholders; build infrastructure, data pipelines, and reusable platform components; mentor engineers; own projects from discovery through production and provide on-call support.

Lead high-impact AI solutions, overseeing architecture and deployment. Partner with stakeholders, coach engineers, and drive project success from inception to launch.