Staff Software Engineer, AI/ML

DigitalOcean Seattle Officefull timeOn Site

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senior

$217k–$271k

H1B: yes

DigitalOcean The Staff Software Engineer, AI/ML at DigitalOcean leads the technical direction for feedback-driven learning in agentic AI systems, including reward modeling and reinforcement learning. This senior IC role involves running large-scale experiments and shipping research into production.

Responsibilities

Own the feedback learning roadmap
Define and execute the applied research agenda for feedback-driven agentic AI — from reward modeling and preference optimization to online learning and human feedback loops.
Translate user feedback, human evaluation data, and product signals into concrete training and optimization strategies.
Stay close to the research frontier on RLHF, RLAIF, DPO, PPO, GRPO, and related methods and know when to apply them versus when simpler approaches win.
Build production learning systems
Design and implement learning loops that improve agent reasoning, planning, tool use, and action execution over time.
Build evaluation frameworks that measure what matters: reasoning quality, instruction following, task success, safety, and real user outcomes — at both offline and online scale.
Run large-scale experiments that connect model changes to measurable improvements in user experience and business impact.
Provide technical leadership
Set technical direction across modeling, experimentation strategy, evaluation design, and production readiness — without requiring direct management authority.
Partner closely with product, engineering, design, and research teams to move work from prototype to shipped capability.
Communicate complex AI systems clearly to both technical and non-technical stakeholders.

Qualification

Experience with agent evaluationDirect experience with RLHFPrior StaffMaster's or PhD in CSResearch contributions via publications

Required

8+ years of experience building production AI/ML systems

Preferred

Experience with agent evaluation, offline/online experiments, and human feedback loops in production.
Direct experience with RLHF, RLAIF, DPO, PPO, GRPO, or related optimization techniques.
Prior Staff, Senior Staff, Tech Lead, or equivalent senior IC experience.
Master's or PhD in CS, ML, AI, or a related field — or equivalent depth demonstrated through industry work.
Experience with production ML infrastructure: model serving, observability, data pipelines, feature stores, or experimentation platforms.
Research contributions via publications, patents, open-source work, or demonstrated applied research impact in RL, reward modeling, evaluation, or recommendation systems.