Job description

About Fuel Cycle: Fuel Cycle empowers leading organizations with agile research solutions that deliver decision-ready insights — fast, flexible, and fully integrated. As a market research disruptor, our AI-powered Insights Platform is built for speed, precision, and scale. With cutting-edge tools and seamless audience connectivity, we help brands ditch the guesswork and make smarter, customer-led decisions at lightning speed. Why work at Fuel Cycle? Join a high-growth team where curiosity is valued, ownership is encouraged, and your work drives real-world impact. Whether you’re based at our Los Angeles HQ, New York City hub, working remotely across the U.S., or part of our global team in India, you’ll help shape the future of decision intelligence for some of the world’s most iconic brands. Overview: Build real AI agents on a production platform, with senior engineers working alongside you. You will work in Python and FastAPI, build tool-calling agents on AWS Bedrock and AgentCore using the Strands Agents framework, and own technical decisions within the problems you take on, with your scope growing as you do. This role is fully in-office in Los Angeles because our mentorship model depends on it: pair programming, in-person code review, and a mentor a few desks away. We hire for fundamentals, curiosity, and initiative. This is an early-career role. Engineers new to the field are welcome, and so are engineers with a year or two of experience who want to go deeper into AI and agentic systems. You do not need to know our stack on day one. What You’ll Work On: - Design, build, and improve AI agents, deciding how tool-calling loops, model configuration, and multi-step workflows are structured using Strands Agents and AWS Bedrock - Build and integrate MCP tools and servers, and learn how tool names and descriptions shape what an LLM does - Deploy and run agents on Bedrock AgentCore, working with the platform team and your mentor - Build RAG pipelines over pgvector or FAISS, choosing chunking, embedding, and retrieval approaches based on what the results show - Design, test, and iterate on prompts; evaluate answer quality, latency, and token cost, and decide which tradeoffs to make; learn how guardrails are applied in production paths - Evaluate models, tools, and approaches, and recommend which to use, with your reasoning documented - Ship features and bug fixes in FastAPI services (REST and WebSocket APIs backed by PostgreSQL and Redis), deciding how to design, implement, and test them - Contribute to background jobs and queue consumers so agent work runs outside the API request path - Apply our architectural standards, use your judgment on design choices within them, help keep design documents current, and flag where the standards should change - Submit pull requests, respond to review feedback, and review teammates’ code as you grow What You Bring: - Solid Python from coursework, internships, or work: functions, classes, type hints, error handling, testing basics - Data structures and algorithms, with awareness of time and space complexity - SQL fundamentals (joins, aggregations, indexes, schema design); PostgreSQL exposure preferred - REST and HTTP concepts, and how a web API is structured - Git and pull-request workflow - Clear communication, comfort asking questions, and a habit of picking up new tools and asking for help when you’re stuck Preferred: - An agent project built with Strands Agents, LangChain, the OpenAI SDK, or a similar framework (coursework, personal, or professional), including how an LLM chooses and calls tools - Model Context Protocol (MCP): building or using MCP tools and servers - AWS Bedrock or Bedrock AgentCore, or AWS basics such as S3 and SQS - RAG: chunking, embeddings, and vector stores - Prompt engineering: writing, testing, and iterating on prompts - FastAPI or a similar framework such as Flask or Django, SQLAlchemy or another ORM, and a willingness to learn async Python Experience: - Bachelor’s or Master’s in Computer Science or a related field, or equivalent practical experience (bootcamp, self-taught, or on-the-job) - Roughly 0–2 years of software engineering experience; internships, research, and substantial projects count - A track record of learning quickly, shown through coursework, projects, work, open-source contributions, or community involvement Benefits & Perks: Fuel Cycle is committed to supporting the well-being, flexibility, and growth of our team. We offer a competitive and inclusive benefits package that includes: - Comprehensive Health Coverage: Medical, dental, and vision insurance plans - 401(k) with Company Match: Plan for your future with our retirement savings program - Equity Purchase Option: Participate in Fuel Cycle’s long-term success - Flexible Work Schedule: Empowering you to balance life and work - Generous Time Off: - 15 vacation days and 7 sick days per year - 12 company holidays - 4 floating holidays/recharge days to rest or celebrate what matters to you - Paid Parental Leave: Time to bond with your growing family - Monthly Internet & Phone Stipend: Support for remote work setup - Wellness & Lifestyle Perks: Access to tools like Rightway (healthcare navigation), Headspace (mental wellness), Peloton (fitness), and more - Team Connection Perks: - Weekly community lunches, refreshments, and snacks at our LA & NY headquarters Compensation Overview: The expected starting salary range for this position is $90,000 - $110,000. This range represents the typical starting compensation offered to candidates hired into this role. Final base salary will be determined based on a variety of factors, including location, work experience, skills, knowledge, education, and certifications. In addition to base salary, this role is eligible for variable bonus, and may include an equity grant or purchase option. These components make up your total compensation package, which will be reviewed in greater detail during your initial recruiter conversation. Commitment to Diversity, Equity, and Equal Opportunity: At Fuel Cycle, we embrace the values of diversity, equity, and inclusion and are committed to fostering an inclusive company culture. We believe that everyone, regardless of their background or identity, should have equal access to opportunities for growth and advancement. Our selection processes and career pathways are designed to be fair, transparent, and free from bias. We value the unique perspectives and contributions of each team member, knowing that this diverse range of experiences strengthens our team. Fuel Cycle stands firmly against discrimination based on disability, sex, race, gender identity, sexual orientation, religion, national origin, age, veteran status, or any other protected status under the law. Fuel Cycle is an equal opportunity employer and fully comply with the Americans with Disabilities Act (ADA). We will provide reasonable accommodations for qualified applicants and employees with disabilities, as needed, to enable them to perform the essential functions of their job and participate in the application and interview process. If you require accommodations during any part of the application process, please contact us at hr@fuelcycle.com to discuss your needs.

Requirements

Must-haves

Design, build, and improve AI agents, deciding how tool-calling loops, model configuration, and multi-step workflows are structured using Strands Agents and AWS Bedrock
Build and integrate MCP tools and servers, and learn how tool names and descriptions shape what an LLM does
Deploy and run agents on Bedrock AgentCore, working with the platform team and your mentor
Build RAG pipelines over pgvector or FAISS, choosing chunking, embedding, and retrieval approaches based on what the results show
Design, test, and iterate on prompts; evaluate answer quality, latency, and token cost, and decide which tradeoffs to make; learn how guardrails are applied in production paths
Evaluate models, tools, and approaches, and recommend which to use, with your reasoning documented
Ship features and bug fixes in FastAPI services (REST and WebSocket APIs backed by PostgreSQL and Redis), deciding how to design, implement, and test them
Contribute to background jobs and queue consumers so agent work runs outside the API request path
Apply our architectural standards, use your judgment on design choices within them, help keep design documents current, and flag where the standards should change
Submit pull requests, respond to review feedback, and review teammates’ code as you grow
Solid Python from coursework, internships, or work: functions, classes, type hints, error handling, testing basics
Data structures and algorithms, with awareness of time and space complexity
REST and HTTP concepts, and how a web API is structured
Git and pull-request workflow
Clear communication, comfort asking questions, and a habit of picking up new tools and asking for help when you’re stuck
Bachelor’s or Master’s in Computer Science or a related field, or equivalent practical experience (bootcamp, self-taught, or on-the-job)
Roughly 0–2 years of software engineering experience; internships, research, and substantial projects count

Nice-to-haves

SQL fundamentals (joins, aggregations, indexes, schema design); PostgreSQL exposure preferred
An agent project built with Strands Agents, LangChain, the OpenAI SDK, or a similar framework (coursework, personal, or professional), including how an LLM chooses and calls tools
Model Context Protocol (MCP): building or using MCP tools and servers
AWS Bedrock or Bedrock AgentCore, or AWS basics such as S3 and SQS
RAG: chunking, embeddings, and vector stores
Prompt engineering: writing, testing, and iterating on prompts
FastAPI or a similar framework such as Flask or Django, SQLAlchemy or another ORM, and a willingness to learn async Python

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

agileaipythonawsgollmrestapisql

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Pto

Parental Leave

Wellness