{"id":2618422,"url":"https://alion.io/job/digitalocean-staff-software-engineer-aiml","title":"Staff Software Engineer, AI/ML","company":{"id":1115,"name":"DigitalOcean","domain":"digitalocean.com","url":"https://alion.io/company/digitalocean","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":75,"open_postings":38,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":23,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Seattle, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":216800,"max":271000,"currency":"USD","period":"year","gross":null,"usd_annual":271000},"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"AI Agents","optional":false},{"name":"DigitalOcean","optional":false},{"name":"DPO","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"GRPO","optional":false},{"name":"PPO","optional":false},{"name":"Python","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reward Modeling","optional":false},{"name":"RLAIF","optional":false},{"name":"RLHF","optional":false},{"name":"Tool Use","optional":false},{"name":"Feature Store","optional":true},{"name":"Recommender Systems","optional":true}],"status":"live","first_seen_at":"2026-06-24T01:19:46Z","employer_posted_date":"2026-10-02","last_verified_at":"2026-10-17T04:47:38Z","board_verified":true,"closed_at":null,"days_open":115,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":115},"description":"Dive in and do the best work of your career at DigitalOcean. Journey alongside a strong community of top talent who are relentless in their drive to build the simplest scalable cloud. If you have a growth mindset, naturally like to think big and bold, and are energized by the fast-paced environment of a true industry disruptor, you’ll find your place here. We value winning together-while learning, having fun, and making a profound difference for the dreamers and builders in the world. \nBuilding AI agents that take real actions is the easy part. Building agents that get better over time - that learn from feedback, correct mistakes, and optimize toward outcomes users actually care about - is one of the hardest open problems in production AI today.\nThat's what this team works on. As a Staff AI/ML Engineer on our Applied Research team, you'll own the technical direction for feedback-driven learning in DigitalOcean's agentic systems: reward modeling, preference optimization, reinforcement learning, and the evaluation infrastructure needed to measure whether any of it is actually working.\nThis is a senior IC role with broad technical scope. You'll set direction, run experiments at scale, and close the loop between user signals and model behavior - shipping research into production, not just writing it up.\nWhat You’ll Be Doing\nOwn the feedback learning roadmap\nDefine and execute the applied research agenda for feedback-driven agentic AI - from reward modeling and preference optimization to online learning and human feedback loops.\nTranslate user feedback, human evaluation data, and product signals into concrete training and optimization strategies.\nStay 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.\nBuild production learning systems\nDesign and implement learning loops that improve agent reasoning, planning, tool use, and action execution over time.\nBuild evaluation frameworks that measure what matters: reasoning quality, instruction following, task success, safety, and real user outcomes - at both offline and online scale.\nRun large-scale experiments that connect model changes to measurable improvements in user experience and business impact.\nProvide technical leadership\nSet technical direction across modeling, experimentation strategy, evaluation design, and production readiness - without requiring direct management authority.\nPartner closely with product, engineering, design, and research teams to move work from prototype to shipped capability.\nCommunicate complex AI systems clearly to both technical and non-technical stakeholders.\nWhat You’ll Add to DigitalOcean\nWe're looking for engineers who have shipped real learning systems - not just prototyped them. You likely bring:\n8+ years of experience building production AI/ML systems - LLMs, GenAI, agentic systems, recommendation, search, personalization, or applied research at scale.\nHands-on experience improving AI systems through reinforcement learning, reward modeling, fine-tuning, human feedback, or preference optimization - with results you can point to.\nStrong understanding of agentic AI: reasoning, planning, tool use, action execution, instruction following, and self-correction.\nStrong software engineering in Python and at least one production systems language.\nThe judgment to balance model quality, product impact, latency, reliability, cost, and maintainability - and communicate those tradeoffs clearly.\nPreferred Qualifications\nStrong signal\nExperience with agent evaluation, offline/online experiments, and human feedback loops in production.\nDirect experience with RLHF, RLAIF, DPO, PPO, GRPO, or related optimization techniques.\nPrior Staff, Senior Staff, Tech Lead, or equivalent senior IC experience.\nNice to have\nMaster's or PhD in CS, ML, AI, or a related field - or equivalent depth demonstrated through industry work.\nExperience with production ML infrastructure: model serving, observability, data pipelines, feature stores, or experimentation platforms.\nResearch contributions via publications, patents, open-source work, or demonstrated applied research impact in RL, reward modeling, evaluation, or recommendation systems.\nCompensation Range: \n$271,000 - $216,800\n*This is a hybrid role\nJR: 2026-7947\n#LI-Hybrid\nWhy You’ll Like Working for DigitalOcean\nWe innovate with purpose. You’ll be a part of a cutting-edge technology company with an upward trajectory, who are proud to simplify cloud and AI so builders can spend more time creating software that changes the world. As a member of the team, you will be a Shark who thinks big, bold, and scrappy, like an owner with a bias for action and a powerful sense of responsibility for customers, products, employees, and decisions.\nWe prioritize career development. At DO, you’ll do the best work of your career. You will work with some of the smartest and most interesting people in the industry. We are a high-performance organization that will always challenge you to think big. Our organizational development team will provide you with resources to ensure you keep growing. We provide employees with reimbursement for relevant conferences, training, and education. All employees have access to LinkedIn Learning's 10,000+ courses to support their continued growth and development.\nWe care about your well-being. Regardless of your location, we will provide you with a competitive array of benefits to support you from our Employee Assistance Program to Local Employee Meetups to flexible time off policy, to name a few. While the philosophy around our benefits is the same worldwide, specific benefits may vary based on local regulations and preferences.\nWe reward our employees. The salary range for this position is based on market data, relevant years of experience, and skills. You may qualify for a bonus in addition to base salary; bonus amounts are determined based on company and individual performance. We also provide equity compensation to eligible employees, including equity grants upon hire and the option to participate in our Employee Stock Purchase Program.\nDigitalOcean is an equal-opportunity employer. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.\nApplication Limit: You may apply to a maximum of 3 positions within any 180-day period. 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