Software Engineering Manager, Public Sector
$194,400–$243,000 year
On-siteSan Francisco, California, United States
Job Summary
Lead a team of 6-8 engineers by recruiting high performers, driving productivity, and providing mentorship on Generative AI projects. Split time between technical planning and execution, collaborating with product and security teams to define, design, and execute strategic roadmaps for backend systems and distributed platforms serving Federal customers. Navigate complex public sector compliance requirements while delivering scalable machine learning infrastructure that empowers mission owners. Participate actively in customer engagements to translate technical concepts to non-technical stakeholders and shape the platform roadmap for Scale AI's Federal business. Requires TS/SCI clearance, 5+ years of engineering experience, and ability to commute to office three times per week.
Required Qualifications
- Must be able to commute to office three times per week
- Have or ability to obtain a TS/SCI clearance
- 5+ years of full-time engineering experience, post-graduation
- 2+ years of prior engineering management or equivalent experience and has managed an engineering team
- Have extensive experience in software development
- Experience scaling products at hyper-growth startups
- Extremely strong track record as an individual contributor
- Show a track record of mentoring and leading teams in successful projects
- Possess excellent communication and collaboration skills, and the ability to translate complex technical concepts to non-technical stakeholders
Desired Qualifications
- Excitement to work with AI technologies and their applications for the public sector
- TS/SCI Clearance
- Deep technical knowledge of Software Development, willing to get deep into the weeds to solve problems alongside the team
- Have experience with AI platforms and technologies, including generative models and LLMs
- Have previous experience in government or government facing technology roles
- Experience with cloud-native technologies, full stack development, data engineering, and ml ops infrastructure
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