Job description

How to Apply Provide the following required materials: • Resume • Cover Letter – In your application, you must submit a cover letter. Please provide brief, concrete examples for the following questions. We value specific technical details, tool names, and honest assessments of where technology succeeds and fails. • Describe a project where you used an AI tool (e.g., ChatGPT, Claude, Cursor, GitHub Copilot, etc) to write or significantly speed up your coding. What were you building, what was your specific approach to instructing the AI, and where did the AI fall short? • All LLMs have specific coding quirks and repetitive errors. Name the specific model you use most (e.g., Opus, GPT-5.x) and describe a highly specific, recurring syntax or logic error it makes that drives you crazy. How do you work around it? • If a hot new open-source AI model gets released tomorrow and you want to test it out locally on your own laptop or machine (not just using a web browser), how would you set that up? What specific software, tools, or frameworks would you use to get it running? Location Your home office or our Waltham, MA HQ or hybrid Qualifications Hard Requirements • Solid grasp of agent architectures, prompt engineering, and MCP or tool-use patterns • Hands-on experience with local/open-source LLMs: you've run them, tuned them, deployed them • Comfortable working in Linux environments and debugging at the command line • Strong communicator: you can explain what's broken and why to both engineers and non-engineers • This is not a role for someone who has tried out some youtube tutorial videos on their own. You need to have some real experience implementing this. Helpful Background • 2+ years Python development — real shipped and used code • Experience with RAG pipelines or vector databases • Familiarity with REST APIs and integration patterns • Docker comfort • Background in storage systems or HPC environments (this helps more than you think) Working Style • You can context-switch between deep development work and supporting a customer mid-debug • You're comfortable being the person who figures things out when there's no playbook yet About Starfish Storage Starfish Storage is an established company that maintains a startup culture mindset. We cater to a diverse client base, serving industries such as life sciences, artificial intelligence, and research computing. Our technology is critical to modern organizations, helping customers organize, manage, and move the tens of billions of files central to their operations. Most AI work right now is happening in the cloud, with managed APIs, and relatively clean data. Some of our customers can't do that: they're operating in classified or regulated environments with filesystem metadata spanning billions of files. The AI problems here are genuinely hard, the deployments are genuinely constrained, and the work actually matters to science and research. If you want AI work that's genuinely constrained, genuinely consequential, and can't phone home to OpenAI — this is it. Job Overview Starfish manages petabytes of unstructured data for national labs, research institutions, fintech, and pharma, the kind of data that doesn't fit neatly into databases or cloud buckets. We're building an AI-native layer on top of that: MCP servers, agentic workflows, local LLM deployments, and tools that let researchers and engineers actually talk to their storage infrastructure. We're tackling hard questions too: should an agent be permitted to move 2PB of data autonomously? How do you govern that? In this role, you'll build and ship MCP servers, deploy and tune local LLMs, write custom agents for real customers with real petabyte-scale problems, and get them running in environments where "just use the API" isn't an option. You'll also support customers when things don't work: which means you need to actually understand what you've built. Responsibilities and Duties include: MCP & Agent Development • Build and expand MCP servers that expose Starfish's data management capabilities to LLM-powered workflows • Develop intelligent agents that can reason over storage metadata at scale — including local LLM deployment and configuration using vLLM, Ollama, or similar • Create workflow automation and chat-based interfaces for querying and acting on Starfish data • Build proof-of-concept implementations and turn them into production-grade features Customer-Facing Technical Work • Support customers through installation, configuration, and troubleshooting of AI features and integrations • Get on calls with technical teams, understand their use cases, and translate requirements into working agents • Document what you learn so the next person doesn't have to figure it out from scratch Benefits We recognize that satisfaction and well-being are essential to long-term sustainability and business success. Full-time employees are eligible for the following benefits: • Salary with potential for future commissions • Multiple health insurance options • Medical FSA and Dependent Care FSA • Dental insurance • Vision insurance • 401(k) savings plan with employer matching • Employer-sponsored long-term disability insurance • Paid holidays and PTO (increasing with tenure) • Discounted health club membership • Many opportunities for growth Equal Opportunity Employer Starfish Storage provides equal employment opportunities (EEO) to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, disability, genetic information, marital status, amnesty, military service, or veteran status, in accordance with applicable federal, state, and local laws. This position may require a background check. Salary 140k to 210k

Requirements

Must-haves

Your home office or our Waltham, MA HQ or hybrid
Hard Requirements
Solid grasp of agent architectures, prompt engineering, and MCP or tool-use patterns
Hands-on experience with local/open-source LLMs: you've run them, tuned them, deployed them
Comfortable working in Linux environments and debugging at the command line
Strong communicator: you can explain what's broken and why to both engineers and non-engineers
This is not a role for someone who has tried out some youtube tutorial videos on their own. You need to have some real experience implementing this.
Helpful Background
2+ years Python development — real shipped and used code
Experience with RAG pipelines or vector databases
Familiarity with REST APIs and integration patterns
Docker comfort
Background in storage systems or HPC environments (this helps more than you think)
Working Style
You can context-switch between deep development work and supporting a customer mid-debug
You're comfortable being the person who figures things out when there's no playbook yet
MCP & Agent Development
Build and expand MCP servers that expose Starfish's data management capabilities to LLM-powered workflows
Develop intelligent agents that can reason over storage metadata at scale — including local LLM deployment and configuration using vLLM, Ollama, or similar
Create workflow automation and chat-based interfaces for querying and acting on Starfish data
Build proof-of-concept implementations and turn them into production-grade features
Customer-Facing Technical Work
Support customers through installation, configuration, and troubleshooting of AI features and integrations
Get on calls with technical teams, understand their use cases, and translate requirements into working agents

Nice-to-haves

None flagged.

Application prep

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

Keywords to mirror

aipythonrestdockerllmapi

Platform: trakstar
Effort: medium

Benefits

Benefits detected in the listing.

Health Insurance

Dental

Vision

401(K)

Pto