Requirements

An ideal candidate can demonstrate previous success in:

• Python-centered development
• Building AI agents, or applications on top of Gen AI solutions
• High scores in math coursework involving Linear Algebra, Statistics, or Econometrics
• Quantitatively comparing model performance and explaining the tradeoffs between them in particular problems to technical and business audiences
• Building lightweight front ends to demonstrate the functionality and visualize performance of their applications, such as with React or Jupyter notebooks
• Strong knowledge of version control and Git commands
• Using the AWS suite of products, particularly for serverless and automated workflows
• Designing experiments
• Cross-functional working relationships with engineers and non-technical stakeholders

The person who joins our team should also be excited to:

• Surfacing analytics insights to users across multiple interfaces (Blog, internal apps, external-facing APIs)
• Best-in-class tools for automating tasks in our financial technology platform
• Supporting economics research in real estate and housing

WHAT WE'RE LOOKING FOR

A successful applicant for this position will be:

• Deeply experienced in modern ML and AI systems, including OCR, RAG, LLM fine-tuning, prompt engineering, and AI agents
• Highly proficient in Python, with working knowledge of GCP infrastructure and modern ML tooling (e.g. Pandas, HuggingFace, Weights & Biases)
• Intensely product-minded, with a focus on translating models into thoughtful, borderline magical features
• Rapidly iterative, able to design smart tests, analyze outcomes, and refine features quickly
• Ownership-oriented, taking end-to-end responsibility for code quality, feature reliability, and production issues when they arise
• Collaborative and generous, willing and able to partner closely with cross-functional teammates
• Passionate about the potential of AI to build great user experiences; experience in the tax domain is a plus but not required

What We’re Looking For
This role is a great fit if you are a product-minded engineer who thrives on building with the latest AI technology.

• Demonstrated experience shipping full-stack AI products, from backend infrastructure (Python, Postgres) to production LLM applications.
• A strong product intuition and deep intellectual curiosity, with a passion for building 0-to-1 experiences in a fast-paced startup environment. You own problems end-to-end.
• A pragmatic approach to LLM safety and reliability, with hands-on experience implementing guardrails, red-teaming, and creating evaluation frameworks to manage trade-offs between performance, cost, and latency.
• Fluency with AI-assisted engineering tools (e.g., Cursor, Copilot) to accelerate your development workflow.

Nice to Haves

• Experience with our front-end stack (TypeScript, React).
• Familiarity with deploying AI workloads on a major cloud platform (AWS, etc.).

Responsibilities:

• Design, develop, and deploy AI systems that address complex challenges in manufacturing applications and deliver measurable value to users.
• Build and optimize agentic AI systems that can autonomously execute tasks, interact with external tools and APIs, and make intelligent decisions.
• Craft and refine prompts for large language models, employing techniques such as few-shot learning, chain-of-thought reasoning, and instruction tuning to optimize model performance and reliability.
• Collaborate with cross-functional teams including data scientists, software engineers, and product managers to identify AI opportunities, define requirements, and deliver integrated solutions.
• Monitor and maintain deployed AI systems, troubleshooting issues, optimizing performance, and ensuring scalability and reliability in production environments.
• Stay current with emerging AI research and technologies, evaluating new approaches and tools that could enhance our capabilities and recommending strategic directions for adoption.
• Document technical implementations, create knowledge-sharing resources, and mentor team members on AI engineering best practices and prompt optimization techniques.
• Validating results in real-world deployments with our customers
• Engaging with the technical community to present results externally, keep up to date on recent advances, and advance the state of the art

Preferred Experience:

• 3-5 years professional experience as an AI/ML Engineer and advanced degrees (M.S. or PhD. in AI, Machine Learning, CS Theory and Algorithms, Computer Science with ML/AI Planning focus, Economics, Operations Research, or related fields)

  • Experience with Agentic AI is a strong plus

• Proficient with Python, including most common AI frameworks and protocols including ADK, MCP, Langchain and exposure to ML and Optimization libraries

• Experience with SQL and GCP is a plus.

• Enthusiasm to own projects end-to-end; from experimentation to customer delivery

Who You Are:

1–5 years of experience as an AI Engineer, ML Engineer, Data Scientist, or Software Engineer with AI/LLM focus.
Practical experience with LangChain, LlamaIndex, AutoGen, or similar frameworks.
Experience with prompt engineering.
Familiarity with vector databases (Pinecone, Weaviate, FAISS, Milvus).
Experience with cloud platforms like GCP and ML infrastructure tools for AI deployment.
Strong collaborator and communicator who can work across product, engineering, and data teams.
Proactive, detail-oriented, organized, and self-sufficient, with a self-starter mentality and ownership attitude.
Works quickly without sacrificing quality to find creative technical solutions.
Excited to work at a fast-paced, scaling organization building cutting-edge healthcare technology.

Great to Have:

Retrieval-Augmented Generation experience.
Proficiency in coding/programming experience (python, node.js) as well as exposure to full-stack integration.
MLOps/LLMOps/NLP model evaluation, deployment, and monitoring performance metrics.
Practice with experimentation techniques.
Proficiency in data manipulation and analysis using tools like pandas, NumPy, and SQL.

Who you are:

• You're a pragmatic problem-solver who finds creative ways around obstacles. You excel at building scalable, data-driven solutions to ambiguous challenges. Whether you're writing SQL queries or researching harmful ingredients in supply chains, you approach problems with curiosity and rigor. You move fast, learn continuously, and believe the climate crisis demands
• Build scalable data infrastructure. Design and maintain pipelines that source, clean, and process large datasets, enabling us to evaluate thousands of brands efficiently.
• Unlock insights through rapid analysis. Conduct targeted analyses that answer critical business questions and help users make informed purchasing decisions.
• Apply AI to real-world problems. Develop agentic tools and leverage LLMs to automate data collection, improve recommendation systems, and scale our ability to assess brand sustainability.
• Collaborate across teams. Partner closely with Engineering and Product to understand our systems deeply, identify improvement opportunities, and scale solutions as we grow.
• Learn and iterate relentlessly. Embrace experimentation, treat learning as the primary outcome of your work, and push the team to move faster while staying focused on impact.
• Measure what matters. Define metrics to track progress, demonstrate system improvements, and quantify the real-world impact of your work.

What you bring:

• Technical foundation. Proficiency in SQL, Python, and building data pipelines, with at least a few years of experience applying this in fast-moving work environments. It’s a plus if you have experience with scraping and cleaning data.
• AI-forward mindset. You're an early adopter of GenAI tools and actively explore how LLMs can transform workflows. Bonus: experience applying AI to large-scale data collection, analysis, recommendations, or consumer applications.
• Structured thinking. You approach problems methodically—seeking out new data sources, validating hypotheses, and building systems that scale. You're skeptical in the right ways and data-driven in your decision-making.
• Bias for action. You thrive in fast-paced, ambiguous environments. When blocked, you find another path. You ship valuable work quickly and aren't afraid to iterate.
• Clear communication. You gather requirements efficiently, synthesize information into clear next steps, and communicate proactively in writing and conversation.
• Passion for sustainability. You're motivated by helping people make informed choices and holding companies accountable. You're familiar with—or eager to decode—corporate sustainability practices. You’re a discerning customer yourself and skeptical of false claims.

You’ll do things like:

• Build MVPs using LLMs, embeddings, and vector databases
• Ship working prototypes that validate new venture ideas
• Move between backend and frontend comfortably
• Help define technical direction for early teams as ideas gain traction

You might be a fit if:

• You’re fluent in Python or TypeScript and can move quickly
• You’ve built with OpenAI, Anthropic, or local models
• You care more about what ships than what’s elegant
• You like the idea of helping birth new products — and new companies
• This is early-stage in every sense. You’ll move fast, break things, and watch some of them turn into real businesses.

What You'll Do

• Orchestrate AI agents (Claude, Cursor, custom MCP tools) to design and deliver backend systems and data services
• Decompose complex engineering projects into AI-executable tasks, manage workflows, and ensure quality outcomes
• Review and validate AI-generated code for correctness, performance, and maintainability
• Design system architectures that AI agents will implement—you set the vision, AI does the execution
• Build and refine AI workflows for common engineering patterns (data extraction, API development, testing)
• Collaborate with AI-assisted teams to establish best practices for AI-driven development
• Own outcomes, not code—you're responsible for working systems, not personally writing every line

A Day in the Life

You're tasked with building a new data extraction service. Instead of opening your editor:

1. You design the architecture—database schema, API contracts, error handling strategy by conversing with a CTO AI
2. You write specifications for AI agents—clear requirements, edge cases, quality criteria
3. You orchestrate AI to implement the service—using Cursor/Claude to generate code, tests, and documentation
4. You review and validate—run tests, check edge cases, ensure it meets production standards
5. You iterate with AI—refine, optimize, and handle any issues that arise

You shipped a production-ready service in a day or two that might have taken a week of hands-on coding. That's AI-forward engineering.

What This Role Is (and Isn't)

This is AI-forward engineering. You'll spend more time designing, directing, and validating than writing code from scratch. You'll work through AI agents to deliver systems at scale and velocity that wouldn't be possible alone.

This is not traditional software engineering. If you love being in the editor all day writing every line yourself, this isn't the role. If the idea of delegating implementation to AI makes you uncomfortable, we're not there yet together.

This requires strong engineering judgment. You need to know what good looks like—solid architecture, clean abstractions, proper error handling, testability. AI will do the work, but you're the one ensuring quality.

You Might Be a Fit If You...

• Have 3+ years of software engineering experience and understand backend systems deeply (even if you won't write all the code)
• Are already working effectively with AI coding assistants (Cursor, Claude, GitHub Copilot, etc.)
• Can think architecturally—you know what good systems look like and can guide AI to build them
• Are comfortable validating code you didn't write—you can review AI output for correctness, performance, and edge cases
• Can decompose complex problems into clear specifications that AI agents can execute
• Trust but verify—you believe AI can do the work, but you know when and how to intervene
• Thrive in ambiguity and rapid iteration—this is a new way of working, and we're figuring it out together

Required Experience

• Python expert with solid AI/ML deployment experience (PyTorch, Hugging Face, LangChain)
• Familiarity with LLMs, computer vision, or VLMs (especially edge inference)
• Experience with drones, robotics, or autonomy frameworks (PX4, MAVSDK, or ROS)
• Comfortable with Docker, Git, and rapid prototyping workflows
• (Nice to have) Experience integrating AI systems into defense, first responder, or industrial contexts

What you'll do

• Collaborate with product leaders to identify customer problems, scope requirements, and implement solutions.
• Build and maintain full-stack product features and systems, end-to-end, ensuring reliability, scalability, and performance.
• Develop observability and metrics to enable smooth operation, proactive issue detection, and continuous improvement.
• Mentor teammates, participate in code reviews, and help shape engineering culture and practices.
• Contribute to building a world-class engineering team through recruiting, mentorship, and knowledge-sharing.

Who you are

• You’re excited about Large Language Models (LLMs) and the opportunity to build systems that support and leverage them.
• You have the ability to ramp quickly on tech stack that features TypeScript, React, Next.js, Node.js, Apollo GraphQL, and PostgreSQL.
• Strong fundamentals in software engineering, with a degree in Computer Science or a related field.
• Thrives in ambiguity, able to independently drive projects and deliver results in a fast-moving environment.
• Clear communicator, capable of articulating technical decisions to both technical and non-technical stakeholders.
• Passionate about building products customers love, ensuring our CRM is intuitive, intelligent, and indispensable.

• You have more than 3 years of experience working as a product manager.
• You possess a strong grasp of AI-agent fundamentals—planning algorithms, tool selection, prompt engineering, and failure-recovery patterns.
• You’ve designed or prototyped autonomous agent workflows or multi-step LLM applications in academic, open-source, or hackathon settings.
• You’re familiar with open-source AI platforms (GitHub, Hugging Face) and have used AI-coding tools daily.
• A background in computer science or related discipline is a plus—but your practical experience with agent frameworks matters more.
• You’re enthusiastic about content creation—anime, fanfiction, TV series, or gaming (e.g., Genshin Impact, BD3).
• You have a builder’s mindset and a passion for using AI to shape the future of entertainment.
  You can speak both of Mandarin and English for internal collaboration with our international teams.

What you’ll do:

• AI Infrastructure & Internal Tools

  • Architect, build, and maintain Jump’s internal AI platform — including model evaluation frameworks, experimentation, context and prompt management systems, and fine-tuning pipelines.
  • Standardize and automate internal tooling: from Claude code org wide setup to orchestration frameworks for AI agents and workflows.
  • Design and deploy RAG systems, vector databases and other foundational components for production-grade AI systems.
  • Define best practices for evaluation and benchmarking of models and agentic workflows (including quality, reliability, latency, cost, and safety).
  • Develop templates and libraries for agentic workflows, memory, and tool use.
  • Partner with the engineering organization to integrate AI agents and features into Jump’s products.
  • Ensure reliability, observability, and scalability of AI systems across environments.

• AI Organizational Enablement

  • Act as an AI subject matter expert — running workshops, demos, and internal talks to help every function apply AI to their workflows.
  • Lead rapid experimentation initiatives — identify and prototype high-impact internal AI use cases, then scale successful ones.
  • Partner cross-functionally with HR, Finance, and Client Services to design and deploy AI automations that save time and increase quality.
  • Maintain an internal AI playbook — a living repository of best practices, design patterns, and how-to guides for prompt engineering, context design, agent building, and evaluation.
  • Mentor engineers and product teams on effective use of LLMs, model selection, and architecture design.

What you’ll bring:

• 10+ years in software engineering or systems architecture, with 1+ years focused on AI/ML, LLMs, and agentic systems.
• Full stack development experience.
• Deep technical expertise with modern AI systems and frameworks — including Foundational models APIs, AWS Bedrock, N8N, Open Source Models, LangChain or similar.
• Proven experience building and deploying AI infrastructure, including evaluation frameworks, monitoring, and agentic workflows.
• Hands-on experience with vector databases, RAG systems, and context management for LLMs.
• Strong proficiency in Python and TypeScript, with experience integrating APIs and orchestration tools.
• Familiarity with cloud infrastructure (AWS), containerization (Docker, Kubernetes), and IaC (Terraform, CloudFormation).
• Experience fine-tuning LLMs.
• Excellent communication and presentation skills — comfortable leading demos, training sessions, and internal talks for both technical and non-technical audiences.
• A passion for teaching, experimentation, and scaling organizational impact through AI.

Nice to have (but not required):

• Experience building multi-agent systems.
• Contributions to open-source AI tools.
• Experience implementing internal developer platforms or productivity tools.

Key Responsibilities:

1. Lead technical design of backend features to support chatbot applications.
2. Establish and execute rigorous testing strategies, particularly automation on test inputs, for robust AI solution validation.
3. Mentor and guide others in the application support team, at times delegating tasks to implement product features.
4. Collaborate across teams to ensure seamless integration of AI solutions within products.
5. Propose and implement innovative strategies, technologies, and AI approaches.

Ideal Candidate:

1. Exposure to chatbot development and natural language processing systems.
2. Experience architecturing new product features in backend systems.
3. Experience implementing or maintaining backend APIs, ideally in Python (FastAPI).
4. Experience testing automation based on chatbot inputs.
5. Comfortable exploring ambiguous problems to propose innovative solutions.
6. Thrives in a diverse, collaborative environment and adapts quickly to change.

Responsibilities:

• Data Warehouse & Analytics: Design and build our analytical data warehouse architecture. Create data models that support business intelligence and reporting needs. Develop insights and dashboards that drive strategic decisions across the organization.
• Data Infrastructure & Pipelines: Maintain the foundational data architecture including ETL pipelines and our core database. Ensure clean data flow from source systems, build real-time streaming capabilities, and create the reliable data foundation that feeds both our analytics and AI systems.
• AI Systems & Evaluation: Build and improve our AI-powered features. Develop evaluation frameworks to measure AI performance, accuracy, and reliability. Create testing pipelines that ensure quality before deployment. Implement monitoring systems to track AI behavior in production and identify areas for improvement.
• AI Context & Integration: Create the knowledge systems our AI features need to understand each client's unique situation. Build retrieval systems for instant access to relevant information, develop semantic search capabilities, and structure data so our AI can provide personalized, contextual responses.

Must-have Qualifications:

• At least (2) years of hands-on experience in data engineering or AI/ML engineering
• Strong data modeling and data warehouse design skills
• Experience building business intelligence solutions and delivering data-driven insights
• Strong Python skills and experience with modern AI frameworks (LangChain, LlamaIndex, or similar)
• Experience building data pipelines and working with SQL databases
• Hands-on experience with LLMs, prompt engineering, or building AI-powered applications
• Self-directed mindset with strong ownership mentality

Nice-to-have Qualifications:

• Experience with modern data warehouse platforms (Redshift, Snowflake, BigQuery)
• Experience with testing and evaluation of AI/ML systems
• Proficiency with BI tools (Tableau, Looker, QuickSight)
• Experience with AI evaluation frameworks and metrics
• Experience with vector databases (Pinecone, Weaviate, ChromaDB)
• Background in building RAG systems or conversational AI
• Experience with AWS data stack (Redshift, Glue, S3, Lambda)
• Knowledge of embedding models and semantic search

Requirements

• 3+ years of experience in software engineering, DevOps, or ML engineering.
• Strong proficiency with Python and experience in containerization (Docker, Kubernetes).
• Familiarity with cloud platforms such as AWS, GCP, or Azure.
• Experience deploying machine learning models using frameworks such as TensorFlow, PyTorch, or ONNX.
• Knowledge of CI/CD pipelines and infrastructure-as-code tools (Terraform, Helm, etc.).
• Understanding of MLOps concepts and best practices for AI deployment and monitoring.
• Excellent communication skills and ability to work in a fast-paced, collaborative environment.

Nice to Have

• Experience with model serving frameworks (e.g., FastAPI, TorchServe, BentoML).
• Familiarity with monitoring tools (Prometheus, Grafana, ELK Stack).
• Background in AI/ML performance optimization and distributed computing.
• Interest in LLM-based systems and agentic AI architectures.

Requirements

Must-have qualifications
• 10+ years’ senior technical leadership (CTO, Head of AI, Distinguished Engineer) with production AI/ML at scale.
• Proven experience in system design, safety/ threat modelling, model/eval frameworks, or shipping LLM/agent features.
• Strong written and verbal communication skills; history of producing decision-ready artifacts (ADRs, diagrams, audits).

Nice-to-have
• Deep experience in regulated health or clinical AI (privacy, DPIA, audit trails).
• Prior fractional/advisory or board-level experience advising engineering/AI teams.

You Might Be a Fit If You…

• Have 4+ years of experience as a software engineer, automation engineer, or technical systems builder

• Have deep experience with agentic architectures, workflow automation platforms (N8n, Zapier, Make), and APIs

• Understand prompt engineering, function-calling, retrieval-augmented generation (RAG), and LLM orchestration

• Are passionate about systems thinking and designing workflows that can scale

• Are an elite debugger and systems optimizer—you can reason through edge cases, flakey agents, and API chaos

• Can balance hands-on work with technical leadership

• Are comfortable in a fast-paced, ambiguous startup environment and thrive under autonomy

• Have a low-ego, high-integrity approach to team collaboration

Key Responsibilities

Requirements

Technical Foundation:

• 3+ years of test automation experience with frameworks you've built, not just used
• Proficiency in modern programming languages and testing paradigms
• Experience with contemporary automation tools and CI/CD pipelines
• Hands-on work with AI tools for code generation and test creation

Mindset & Approach:

• Comfort operating without detailed specifications or existing playbooks
• Ability to translate business needs into comprehensive test strategies
• Natural inclination toward simplification over complexity
• Track record of achieving high quality metrics through systematic approaches

Valuable Experience:

• Building test frameworks for distributed systems
• Performance and chaos engineering
• Mobile & cross-platform test automation
• Contributing to or maintaining open-source testing tools

Key Responsibilities:

1. Lead technical design of backend features to support chatbot applications.
2. Establish and execute rigorous testing strategies, particularly automation on test inputs, for robust AI solution validation.
3. Mentor and guide others in the application support team, at times delegating tasks to implement product features.
4. Collaborate across teams to ensure seamless integration of AI solutions within products.
5. Propose and implement innovative strategies, technologies, and AI approaches.

Ideal Candidate:

1. Exposure to chatbot development and natural language processing systems.
2. Experience architecturing new product features in backend systems.
3. Experience implementing or maintaining backend APIs, ideally in Python (FastAPI).
4. Experience testing automation based on chatbot inputs.
5. Comfortable exploring ambiguous problems to propose innovative solutions.
6. Thrives in a diverse, collaborative environment and adapts quickly to change.