Business Operations - Systems & Automation

Adaptive ML•New York City, NY

About The Position

Context Adaptive ML is building a Reinforcement Learning Operations (RLOps) platform that enables enterprises to specialize and operationalize large language models through fine-tuning, evaluation, feedback loops, and deployment. Our business and engineering motions are highly technical, cross-functional, and collaborative across time zones. As we scale, a growing part of our leverage comes from internal systems, automation, and lightweight tooling that remove friction across go-to-market and delivery. This role is a Swiss-army-knife operator–builder : someone who sits between business, technical teams, and systems, and turns ambiguity into working software, workflows, and internal tools. Role Mission Design, build, and operate internal systems and lightweight applications that remove execution friction and increase organizational leverage across how the company operates. Contribute as an internal Operating System architect , shaping the systems, workflows, and signals that connect business, technical, and people operations into a coherent, scalable way of running the company — without adding process overhead.

Requirements

Strong technical foundation (software engineering, data, or systems background).
Familiar experience with Slack, Hubspot, Apollo.
Experience building automation and internal tools across business functions (RevOps, Sales Ops, Marketing Ops, CS Ops).
Comfort operating in ambiguity and defining problems before solving them.
Ability to ship scrappy, high-leverage solutions that people actually use.
Strong product and systems thinking: understands workflows, incentives, and failure modes.
Clear communication and collaboration across technical and non-technical stakeholders.

Nice To Haves

Experience with workflow automation tools (e.g. n8n, Cursor, internal APIs).
Slack-first tooling, bots, or internal apps.
Familiarity with modern AI workflows (RAG, evaluation, cost trade-offs).
Experience with analytics, dashboards, or lightweight data pipelines.
Curiosity about infrastructure, cost, and performance (e.g. GPUs, compute trade-offs).

Responsibilities

Own and automate cross-functional execution workflows that span how the company operates from first customer interaction through delivery and ongoing engagement, focusing on breakdowns at handoffs, duplication of effort, or unclear ownership across sales, marketing, technical success, and operations.
Work closely with sales, marketing, and technical success as inputs and users of systems , not as the sole focus, to remove friction across pre-sales, post-sales, and go-to-market execution and ensure workflows reflect how work actually happens.
Design and instrument internal systems to surface decision-grade signals around cost, effort, adoption, effectiveness, and capacity — enabling the company to understand where time and resources are spent and where organizational leverage exists.
Improve staffing efficiency and delivery predictability by increasing visibility into technical success and field deployment efforts (capacity, time allocation, bottlenecks), and by automating interfaces between teams rather than adding reporting or process overhead.
Build internal and customer-facing mini-apps that turn repeated bespoke work across GTM and delivery into reusable primitives (e.g. TCO and cost calculators, qualification tools, rollout readiness checklists).
Automate knowledge ingestion and digestio n across internal systems (Slack, docs, tools, marketing assets) to reduce repeated questions
Exercise strong judgment in sequencing by deciding deliberately what not to automate yet, balancing speed of execution, learning velocity, and long-term maintainability.