Adaptive ML

• Contribute to the foundational technology powering Adaptive ML, with support and guidance from the team • Help advance projects by implementing features, running experiments, or improving reliability • Communicate clearly about your work and learn to collaborate in a distributed team environment • Write clear, well-structured code (primarily in Python; exposure to systems programming is a plus, not a requirement) • Help debug issues in distributed or ML-heavy systems • Learn best practices for performance, testing, and robustness • Assist with research on large language models and reinforcement learning • Reproduce and analyze results from recent ML literature • Support empirical experiments and help document findings • Interns are encouraged to explore both areas of engineering and research

• Lead customer-facing workload planning — understanding model usage patterns, expected throughput, and infrastructure constraints to scope solutions accurately from day one. • Own solution architecture in the sales cycle: infra selection, TCO calculation, and performance benchmarking tailored to each prospect’s environment and LLM workloads. • Design and deliver compelling technical demos and proof-of-concept implementations that map Adaptive ML’s capabilities directly to customer pain points and existing infrastructure. • Respond to technical evaluations, RFPs, and security reviews; go deep with engineering and data science counterparts on architecture decisions and integration requirements. • Partner with Account Executives to shape deal strategy, accelerate procurement timelines, and remove technical blockers standing between a prospect and a signed contract. • Own technical onboarding end-to-end — designing integration architectures, working directly with customer engineering teams, and driving time-to-first-value. • Support and continuously optimise live deployments: cost optimisation, performance tuning, and workload expansion across multi-geo and multi-team customer environments. • Be the escalation point for production issues — investigating and debugging problems spanning k8s deployments, Helm configurations, model serving infrastructure, and distributed systems. • Drive workload expansion proactively: surface new use cases, additional model workflows, and untapped product capabilities that create value across your account portfolio. • Conduct regular technical and business reviews with customer stakeholders, translating infrastructure metrics into business impact and building the case for renewal and growth. • Build reusable technical assets — reference architectures, integration guides, runbooks, and demo environments — that scale knowledge and accelerate future deals. • Act as the voice of the customer internally: channel field insights directly to Product and Engineering to shape the roadmap and prioritisation. • Contribute to infra sizing and workload planning discussions alongside Solutions and DevOps colleagues, with particular focus on the NA region (NYC/Toronto coverage).

• Own product areas end-to-end, from problem discovery to delivery and iteration. • Continuously prioritize in an environment where the market and constraints evolve fast. • Be accountable for product quality, bugs, and user-reported issues. • Work directly with highly technical customers to deeply understand workflows, pain points, and edge cases. • Translate complex, technical feedback into clear product decisions. • Write clear, detailed specs and iterate tightly with engineers and ML researchers. • Balance speed with rigor; catch edge cases early. • Prototype early and often (including AI-assisted prototypes) to accelerate learning and iteration. • Make technically complex systems simple and usable without hiding their power. • Adapt communication and product thinking to different audiences.

• Build systematic K8s workflows to deploy our product, either client-side on a variety of infrastructures, or internally for our cloud platform; • you'll work closely with sales and customer success to assist product deployment, pre and post sales (POC, demos, production), both on-prem, in cloud and in our SaaS. • you'll be part of our first line of support for customer escalation, including bugs, security escalations, special events support (scale-outs, large workshops, load tests) • Contribute to deployment support, in particular on North America time zones. This involves both personally joining customer calls to assist onboarding and troubleshooting, but also leading and contributing to our support mechanisms such as ticket triaging and oncall process. • Contribute to our product roadmap, by coordinating between the needs of the Commercial Staff and latest developments from our Technical Staff; • Report clearly on your work to a distributed collaborative team, with a bias for asynchronous written communication.

• Empower ML engineers and developer teams to leverage reinforcement learning • Design and build intuitive SDKs, libraries, APIs, and tooling • Partner with internal teams to refine primitives into documented modules