Forward Deployed Engineer – AI & Product (YC Startup)
Bounty Amount: $11,250 - 16,500
Company Name: Arist
Role Type: Full-Time
Location: US / Remote - With Offices in NY
Salary / Hourly Rate: $150,000 - $220,000 per year
Benefits: US citizen/visa only
Role Information
Role Overview: N/A
Responsibilities: Partner directly with customers to understand their use cases, identify pain points, and translate needs into actionable technical solutions., Design and implement agentic workflows powered by LLMs for real-world task execution., Build customization layers tailored to organization-specific logic and deployment requirements., Deploy and iterate on LLM-powered pipelines with evaluation and feedback loops in production environments., Develop voice-based and message-based agents that drive engagement and adoption., Architect AI pipelines (RAG, prompt engineering, evaluation) and ensure reliable integration into complex infrastructures., Collaborate closely with product and engineering teams to bring field feedback directly into the product roadmap., Communicate clearly with both technical and non-technical stakeholders to align on goals and deliver solutions quickly.
Qualifications: 5+ years of experience in ML (PyTorch, TensorFlow)., 2+ years of hands-on experience working with LLMs (Hugging Face, OpenAI, Anthropic)., Proven experience building and deploying production AI systems, including RAG and vector search., Strong knowledge of prompt engineering, AI safety, and content filtering best practices., Comfort architecting scalable infrastructure that integrates into complex environments., Experience working directly with customers to deploy and iterate on technical solutions in real environments., Excellent communication skills with the ability to explain complex concepts clearly to both technical and non-technical stakeholders., Proven ability to gather feedback, shape product direction, and collaborate effectively with cross-functional teams., Familiarity with Rails is a plus, but not required — strong candidates can ramp up quickly., Experience with REST APIs, PostgreSQL, ActiveRecord, and RSpec., Understanding of frameworks like LangChain or LlamaIndex, or the ability to learn them rapidly., Thrives in ambiguity, learns quickly, and iterates fast in lean environments., Excited to work in a small, high-impact team where communication and ownership are key.
Minimum Requirements: 5+ years of experience in Machine Learning (PyTorch, TensorFlow).,2+ years of hands-on experience working with LLMs (Hugging Face, OpenAI, Anthropic).,Proven track record of building and deploying production AI systems, including RAG and vector search.,Strong understanding of AI safety, prompt engineering, and content filtering best practices.,Excellent communication skills, with experience collaborating directly with users, clients, or cross-functional teams.,Familiarity with Rails is a plus, but not required — strong candidates can ramp up quickly.
Screening Questions: Please share a 2–3 minute video (screen share + voice) walking us through something you’ve built or worked on recently — ideally related to AI, LLMs, or system integration. This is a hard requirement, we cannot move forward with your application without it.,Please share a link to your GitHub or personal portfolio (if applicable):,Are you comfortable building with AI hands-on and working directly with customers to gather feedback, shape features, and help launch new products? Please share an example of when you’ve done this or how you would approach it.
Company Information
About Company: N/A
Culture: N/A
Additional Information
Interview Process: Initial Call (30 min) – Introductory conversation with a recruiter or founder to review background, motivations, and role fit.Technical Deep Dive (60–90 min) – Live discussion with senior engineers covering LLM design scenarios, Rails integration, and system-level problem solving.Practical Exercise / Case Study – Short take-home or live coding assignment focused on building or evaluating an AI workflow.Final Round (60 min) – Cultural and cross-functional interviews with founders/team to assess collaboration, communication, and alignment with mission.
Day to day: You’ll spend your days working directly with customers to design, build, and deploy advanced AI systems that deliver immediate real-world impact. Your time will be split between hands-on development (Rails, ML/LLM workflows), collaborating with client teams to understand their workflows, and rapidly iterating on solutions based on real feedback. Most days blend deep technical problem solving with fast feedback loops from both internal teams and enterprise customers, giving you direct visibility into how your work drives product adoption and impact.
Team: You’ll join a lean, highly technical founding team, reporting directly to the CTO and collaborating closely with the CEO and product leads. The team is composed of engineers with backgrounds in AI/ML, backend systems, and enterprise software, all focused on building production-grade AI enablement tools. You’ll have significant ownership and autonomy, with the ability to shape architecture, standards, and culture.
Growth: Take ownership of core technical deployments from day one., Work closely with enterprise customers to influence product direction in real time., Mentor and support other engineers as the team scales., Build a strong track record of shipping and deploying AI systems at scale — positioning yourself for future leadership roles in engineering, product, or customer success functions.
Ideal Candidate Profile: Innovative Builder — Designs and deploys sophisticated AI workflows that solve complex, real-world enterprise challenges., Forward Deployed Mindset — Thrives on working directly with customers, gathering feedback, and iterating rapidly to solve problems in the field., Product-Minded Communicator — Excels at collaborating with users, clients, and cross-functional teams to shape solutions and drive adoption., Technically Excellent — Brings strong expertise in Rails (REST APIs, PostgreSQL, ActiveRecord, RSpec) and hands-on experience deploying scalable AI systems using modern frameworks and infrastructure., Collaborative and Fast-Moving — Thrives in ambiguity, learns new frameworks (LangChain, LlamaIndex, etc.) quickly, and delivers results at startup speed. Works seamlessly with founders, engineers, and product teams to iterate rapidly.Examples of Candidates (do not contact)Yutong XueAndrew R. ReedZion BadashRyan Rishi, Examples of Candidates (do not contact)Yutong XueAndrew R. ReedZion BadashRyan Rishi, Yutong Xue, Andrew R. Reed, Zion Badash, Ryan Rishi