{"id":14543,"url":"https://alion.io/job/plaid-staff-machine-learning-engineer-research-scientist-dfai","title":"Staff Machine Learning Engineer (Research Scientist) - DFAI","company":{"id":4515,"name":"Plaid","domain":"plaid.com","url":"https://alion.io/company/plaid","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":95,"open_postings":5,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":72,"computed_at":"2026-10-19T05:45:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States","New York, United States","Seattle, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":249120,"max":367920,"currency":"USD","period":"year","gross":null,"usd_annual":367920},"salary_estimate":null,"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Fine-tuning","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Transformers","optional":false},{"name":"Feature Store","optional":true}],"status":"live","first_seen_at":"2026-05-26T23:47:12Z","employer_posted_date":"2026-05-26","last_verified_at":"2026-10-19T18:34:08Z","board_verified":true,"closed_at":null,"days_open":145,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":145},"description":"We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam.\nWe are the Data Foundation & AI team within Plaid’s Data organization. Our mission is to build the shared ML and AI infrastructure that powers intelligent capabilities across Plaid’s product suite. We develop the foundational systems, models, and data assets that transform Plaid’s unique financial network data into scalable, general-purpose representations that teams across the company can leverage. Our work spans the full ML lifecycle - from large-scale data curation and model pretraining to production serving, evaluation, and monitoring. As part of the team, you’ll work at the intersection of machine learning infrastructure, applied AI, and distributed systems, helping establish the core AI platform that enables innovation across Plaid.\nAs a Staff Machine Learning Engineer, you will lead the technical strategy and development of Plaid’s foundation models, driving key decisions across pretraining objectives, model architecture, and fine-tuning approaches that power a wide range of downstream product applications. You will serve as the technical lead for the full machine learning lifecycle, overseeing everything from data curation and experimentation to production deployment, feature management, and observability. In this role, you will establish rigorous evaluation frameworks to measure model performance across diverse use cases and build scalable, repeatable pipelines that translate research into production impact. You will also partner closely with teams across the organization to define how products integrate with and adapt foundation models, enabling reusable ML infrastructure and reducing duplicated modeling efforts. As a senior technical leader, you will mentor engineers across experience levels, elevate engineering and experimentation standards, and communicate technical advancements both internally and externally as a representative of Plaid’s AI and machine learning capabilities.\nResponsibilities:\nOwning the end-to-end technical strategy for a foundation model built on one of the world's richest financial datasets, from pretraining architecture to production serving.\n\nDoing research that ships: driving decisions from experimentation through production systems that serve real customers and power multiple product teams.\n\nWorking across the full ML stack, including pretraining objectives, architecture design, distributed training, serving infrastructure, monitoring, and cross-team integration.\n\nSetting technical direction and mentoring a high-caliber team, with your work amplifying the capabilities of engineers and product teams across Plaid.\n\nHelping hundreds of millions of consumers achieve greater financial freedom through the ML capabilities you build and ship.\n\nQualifications:\nMS: 7-12+ years of industry experience with a demonstrated track record of technical leadership and production delivery.\n\nPhD: 5-9+ years of industry experience with evidence of technical leadership (tech lead, principal/staff-equivalent roles) and end-to-end production ownership.\n\nPrior technical leadership experience (tech lead, principal, or staff) with demonstrated cross-team influence and mentorship.\n\nDeep expertise in Transformers/LLMs/Foundation Models, including large-scale training or domain adaptation.\n\nEnd-to-end production ownership; proven track record shipping models through training, serving, monitoring, and iteration in live environments.\n\nDistributed training experience and strong Python + software engineering fundamentals at a staff level.\n\nAbility to drive technical alignment across teams: setting standards, defining integration patterns, and influencing beyond your immediate scope.\n\nFintech / financial data domain experience - Nice to have\n\nExternal publications or open-source contributions - Nice to have\n\nExperience defining ML platform capabilities (serving infra, feature stores) used across multiple teams. - Nice to have\n\nOur mission at Plaid is to unlock financial freedom for everyone. To support that mission, we seek to build a diverse team of driven individuals who care deeply about making the financial ecosystem more equitable. We recognize that strong qualifications can come from both prior work experiences and lived experiences. We encourage you to apply to a role even if your experience doesn't fully match the job description. We are always looking for team members that will bring something unique to Plaid!\nPlaid is proud to be an equal opportunity employer and values diversity at our company. We do not discriminate based on race, color, national origin, ethnicity, religion or religious belief, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, military or veteran status, disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state, and local laws. Plaid is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance with your application or interviews due to a disability, please let us know at .\nPlease review our Candidate Privacy Notice here.\nAdditional compensation in the form(s) of equity and/or commission are dependent on the position offered. Plaid provides a comprehensive benefit plan, including medical, dental, vision, and 401(k). Pay is based on factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience and skillset, and location. 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