Member of Technical Staff - Applied ML

As an ML Engineer at Basis, you’ll own end-to-end projects that bring intelligence into production. You’ll act as the responsible party for systems that help our agents reason, plan, and evaluate themselves — meaning you’ll scope, build, and deliver from first principles. You’ll have full autonomy: plan your projects, define success, run experiments, and decide when your system is ready to ship.

You’ll move fast, instrument deeply, and design for clarity — building the scaffolding that lets models act safely and improve continuously. This is a role for engineers who want to operate like researchers and builders at once: reasoning, experimenting, and shipping systems that get smarter over time.

What you’ll be doing:

• Build and evolve our agent systems
• Design and iterate multi-agent architectures that automate real accounting workflows.
• Encode autonomy boundaries, tool usage, and fallback behaviors that make agents safe and reliable.
• Manage context and memory for coherence across steps; plan and execute agent loops with measurable success criteria.
• Route, evaluate, and optimize models under real-world constraints (latency, cost, accuracy).

2. Design evaluation and experimentation frameworks

• Build scalable evaluation pipelines (offline + online) that run hundreds of experiments automatically.
• Define golden tasks, labeling strategies, and metrics that make performance measurable and comparable.
• Instrument the stack to detect regressions, track error taxonomies, and drive closed-loop improvement.
• Use data and experiments to drive product and architectural decisions—not just intuition.

3. Engineer for context and retrieval

• Architect prompt stacks and instruction hierarchies that structure model reasoning.
• Build retrieval and indexing pipelines that surface relevant context efficiently.
• Parse messy documents into structured representations that agents can reason about.
• Design guardrails and validation layers to keep behavior safe and deterministic.

4. Operate as an RP — plan, build, deliver

• Scope your projects with clarity; write concise specs and architecture docs that eliminate ambiguity.
• Build, test, and instrument your systems end-to-end.
• Communicate progress clearly: what’s built, what’s learned, what’s next.
• Collaborate tightly within your pod — teaching, unblocking, and sharing learnings as you go.