{"schemaVersion":"jobsearcher.job.v1","id":"18b5caed5d7ccd059cb1e17b","url":"https://jobsearcher.com/jobs/18b5caed5d7ccd059cb1e17b","canonicalUrl":"https://jobsearcher.com/jobs/18b5caed5d7ccd059cb1e17b","title":"Machine Learning Engineer","description":"🚀 AI/ML Engineer (Applied AI - Government & Enterprise)Location: Hybrid 3 days/week on-site in San Francisco or NYC Compensation: $200,000 – $350,000 Base Salary + Competitive EquityVisa Status: Open to US Visa Transfers (OPT, H-1B, etc.)🌟 The CompanyOur client is an elite, high-growth applied AI startup ($55M Series A backed by top-tier tech leaders including Andrej Karpathy, Patrick Collison, and Elad Gil).In less than two years, they have grown to over 140 employees and hit $20M+ in revenue by deploying production-grade AI systems directly into high-stakes environments—automating complex, real-world workflows for international governments, healthcare systems, and Fortune 500 energy leaders.💡 The RoleThis is an applied, product-driven AI engineering role. You will bridge the gap between cutting-edge LLM research and real-world deployment, building AI agents, reasoning systems, and complex data pipelines that solve critical, manual problems globally.You will own projects end-to-end—from post-training and prompt engineering down to production code and direct engagement with government officials and enterprise leaders.🎯 Key Responsibilities• Design & Deploy: Build and ship advanced LLM architectures, AI agents, and RAG systems into production environments for global nation-states and enterprise clients.• Optimize & Scale: Build scalable data pipelines, design robust ML eval frameworks, and optimize models for real-world reliability and accuracy.• Direct Engagement: Interact directly with customer leadership and government stakeholders to understand domain challenges and deliver custom AI solutions.• Full-Stack Impact: Wear multiple hats across software engineering, product direction, and customer engagement in a high-velocity startup setting.💻 Tech Stack• Languages & Frameworks: Python, PyTorch, JAX, TensorFlow• AI/ML Architecture: LLMs, RAG, AI Agents, Reasoning Models, Data Pipelines• Eval & Testing: Modern ML Evaluation Frameworks, CoderPad🛠️ What We're Looking For• 3 – 10 Years Experience: Applied, product-focused AI/ML engineering background (building production applications in Python).• Applied Product Focus: Hands-on experience deploying LLMs, RAG, or AI agents to external end-users (this is NOT a pure research, MLOps, or platform infra role).• Proven Business Impact: Ability to clearly articulate and quantify the commercial or operational impact of your ML systems (e.g., revenue generated, time saved, accuracy gains).• Startup Credential: Experience in high-velocity startup environments (e.g., Glean, Cohere, Together AI, Databricks) or fast-paced product teams at select tech firms (e.g., DoorDash, Amazon, TikTok, Stripe). Ex-founders and founding engineers are highly valued.• Education: BSc/MSc in Computer Science (top CS programs preferred for junior/mid-level profiles).🔴 Red Flags / Out of Scope• Purely research-heavy or PhD-focused profiles with no product/production shipping experience.• MLOps, platform, or infrastructure-only engineers.• Candidates exclusively from traditional corporate/legacy engineering cultures (e.g., Oracle, Salesforce, big banks).🎁 Why Join• Explosive Growth: Joined a 140-person team that hit $20M+ revenue in year one.• Elite Backing: Backed by legendary Silicon Valley founders and investors ($55M Series A).• Real-World Footprint: Your code directly powers critical government, energy, and healthcare infrastructure globally.• End-to-End Autonomy: High accountability, zero bureaucracy, and high-impact equity.🧩 Interview Process1. Recruiter Screen (30 mins): High-level screen with the internal team assessing background, startup velocity fit, communication skills, and project impact.2. ML Technical Interview (60 mins): Real-world ML problem-solving session testing how you translate a business scenario into an ML problem, define evaluation metrics, and architect the solution.3. Live Coding Interview (60 mins): Virtual CoderPad session testing Python fundamentals, debugging, and practical engineering skills (applied, non-Leetcode style problem; AI tools allowed).4. Onsite Interview (3.5 Hours):• Two technical screen rounds• Past project deep-dive with an Engineering Manager (evaluating startup pace, technical depth, and cross-functional collaboration)• Lunch, office tour, and culture alignment chat with the team","company":"Protech Talent","rawCompany":"protech talent","city":"Menlo Park","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-08-14T15:57:15.022Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Machine Learning Engineer","description":"🚀 AI/ML Engineer (Applied AI - Government & Enterprise)Location: Hybrid 3 days/week on-site in San Francisco or NYC Compensation: $200,000 – $350,000 Base Salary + Competitive EquityVisa Status: Open to US Visa Transfers (OPT, H-1B, etc.)🌟 The CompanyOur client is an elite, high-growth applied AI startup ($55M Series A backed by top-tier tech leaders including Andrej Karpathy, Patrick Collison, and Elad Gil).In less than two years, they have grown to over 140 employees and hit $20M+ in revenue by deploying production-grade AI systems directly into high-stakes environments—automating complex, real-world workflows for international governments, healthcare systems, and Fortune 500 energy leaders.💡 The RoleThis is an applied, product-driven AI engineering role. You will bridge the gap between cutting-edge LLM research and real-world deployment, building AI agents, reasoning systems, and complex data pipelines that solve critical, manual problems globally.You will own projects end-to-end—from post-training and prompt engineering down to production code and direct engagement with government officials and enterprise leaders.🎯 Key Responsibilities• Design & Deploy: Build and ship advanced LLM architectures, AI agents, and RAG systems into production environments for global nation-states and enterprise clients.• Optimize & Scale: Build scalable data pipelines, design robust ML eval frameworks, and optimize models for real-world reliability and accuracy.• Direct Engagement: Interact directly with customer leadership and government stakeholders to understand domain challenges and deliver custom AI solutions.• Full-Stack Impact: Wear multiple hats across software engineering, product direction, and customer engagement in a high-velocity startup setting.💻 Tech Stack• Languages & Frameworks: Python, PyTorch, JAX, TensorFlow• AI/ML Architecture: LLMs, RAG, AI Agents, Reasoning Models, Data Pipelines• Eval & Testing: Modern ML Evaluation Frameworks, CoderPad🛠️ What We're Looking For• 3 – 10 Years Experience: Applied, product-focused AI/ML engineering background (building production applications in Python).• Applied Product Focus: Hands-on experience deploying LLMs, RAG, or AI agents to external end-users (this is NOT a pure research, MLOps, or platform infra role).• Proven Business Impact: Ability to clearly articulate and quantify the commercial or operational impact of your ML systems (e.g., revenue generated, time saved, accuracy gains).• Startup Credential: Experience in high-velocity startup environments (e.g., Glean, Cohere, Together AI, Databricks) or fast-paced product teams at select tech firms (e.g., DoorDash, Amazon, TikTok, Stripe). Ex-founders and founding engineers are highly valued.• Education: BSc/MSc in Computer Science (top CS programs preferred for junior/mid-level profiles).🔴 Red Flags / Out of Scope• Purely research-heavy or PhD-focused profiles with no product/production shipping experience.• MLOps, platform, or infrastructure-only engineers.• Candidates exclusively from traditional corporate/legacy engineering cultures (e.g., Oracle, Salesforce, big banks).🎁 Why Join• Explosive Growth: Joined a 140-person team that hit $20M+ revenue in year one.• Elite Backing: Backed by legendary Silicon Valley founders and investors ($55M Series A).• Real-World Footprint: Your code directly powers critical government, energy, and healthcare infrastructure globally.• End-to-End Autonomy: High accountability, zero bureaucracy, and high-impact equity.🧩 Interview Process1. Recruiter Screen (30 mins): High-level screen with the internal team assessing background, startup velocity fit, communication skills, and project impact.2. ML Technical Interview (60 mins): Real-world ML problem-solving session testing how you translate a business scenario into an ML problem, define evaluation metrics, and architect the solution.3. Live Coding Interview (60 mins): Virtual CoderPad session testing Python fundamentals, debugging, and practical engineering skills (applied, non-Leetcode style problem; AI tools allowed).4. Onsite Interview (3.5 Hours):• Two technical screen rounds• Past project deep-dive with an Engineering Manager (evaluating startup pace, technical depth, and cross-functional collaboration)• Lunch, office tour, and culture alignment chat with the team","datePosted":"2026-08-14T15:57:15.022Z","dateModified":"2026-08-14T15:57:15.022Z","hiringOrganization":{"@type":"Organization","name":"Protech Talent","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Menlo Park","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"18b5caed5d7ccd059cb1e17b"},"url":"https://jobsearcher.com/jobs/18b5caed5d7ccd059cb1e17b"}}