Founding AI/ML Engineer
Quick Summary
About the Company Our client is a seed-stage public safety technology startup that helps law enforcement search evidence and solve crime. Police and prosecutors across the U.S.
Our client is a seed-stage public safety technology startup that helps law enforcement search evidence and solve crime. Police and prosecutors across the U.S. use the platform to accelerate investigations into homicides, cold cases, and other major offenses by reducing digital evidence overload. The founding team includes engineers who have previously built national-scale data and defense systems.
Backed by top-tier investors, the company is growing fast with live deployments and paying agency customers. The mission is clear: bring technical excellence to public safety.
About the Role
~1 min readYou'll be an early engineer shaping the company's AI backbone, processing massive video, audio, and text datasets to surface investigative leads. The work spans backend infrastructure and applied ML: building retrieval pipelines, integrating LLMs/VLMs, and deploying secure, distributed systems. You should be comfortable shipping end-to-end, from data flow design to production deployment in sensitive environments, and thrive in fast-paced, high-impact settings.
We're looking for a backend engineer with deep experience and interest in applying AI in the real world.
- Backend Grounding: You can build resilient, secure distributed systems, with comfort across programming languages (especially Python, React, etc.) and infrastructure providers.
- AI Depth: Experience deploying LLMs, VLMs, and/or other ML in real-world settings. Bonus if you've worked with or are excited by agentic experiences: systems that reason, plan, and take actions across data.
- Data Versatility: You've designed RAG or similar pipelines for unstructured data, and know how to build for structured data too (query planning, semantic modeling, or structured reasoning).
- Adaptability: The team moves fast, and new models and approaches to infrastructure are constantly coming online. You are naturally curious and keep up with the latest in AI.
- Mission-Driven: Excited to contribute to U.S. government competence and public safety.
- Owner-Builder Mindset: Bias toward action; thrives in extremely fast-paced environments. Looking to do the best work of your life.
- Comfort with Ambiguity: This is an early-stage startup with many exciting questions to be answered; you run toward unasked and unanswered questions.
- Location: New York City, NY or San Francisco, CA (in-person at least 3 days per week, with up to 25% travel)
- Employment: Full-time
- Experience: 3+ years
- Salary: $165,000 – $250,000 USD per year, depending on experience
- Visa Sponsorship: Not available
- Clearance: Must be willing to undergo an FBI background check and CJIS clearance (sponsored by the company)
What We Offer
~1 min read- Intro call with a co-founder
- Coding interview (LeetCode-style)
- Systems interview
- Final behavioral interview
- Offer
Please include answers to the following questions with your application:
- Do you have valid U.S. work authorization and are you willing to undergo an FBI background check / CJIS clearance process?
- Are you able to work in-person from NYC or SF at least 3x/week?
- Are you OK with 25% travel on the job?
- Do you have experience with RAG, speech-to-text, or computer vision?
- Why are you interested in this role and joining this company?
- Do you see yourself closer to the Research or Applied side of ML systems?
- Do you see yourself as proficient in general backend engineering?
- Do you have strong English communication skills? This is a requirement due to the company's customer base.
Location & Eligibility
About this job
- Pay per year
- $165,000–$250,000 a year
- 2% below the $211,200 median for Machine Learning Engineer jobs on Jobera (898 salaries).
- Who can apply
- United States
- Posted
- Today
Listing Details
- First seen
- October 22, 2026
- Last seen
- October 22, 2026
Posting Health
- Days active
- 0
- Repost count
- 0
- Trust Level
- 68%
- Scored at
- October 22, 2026
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