
Forward Deployed Engineer, U.S. Government
Posted 3 days ago
The role in short
Build and deploy agentic AI systems and LLM applications for U.S. Government missions, working directly with partners to translate operational needs into production software.
About micro1
micro1 is a data lab that helps AI labs & Enterprises train foundational models and agents. We provide frontier evaluations and reinforcement learning environments to improve LLM capabilities, as well as contextual evaluations to monitor and improve AI agents in enterprise settings.
Our AI recruiting agent sources and vets domain experts for our data platform that enables rapid production of high-quality training data. Our research spans high-value, specialized domains (legal, medical, finance, coding, etc.), where expert-level data is the hardest to produce and most valuable for advanced model performance. Our pipeline performance system ensures both quality and velocity.
Job Title: Forward Deployed Engineer, U.S. Government
Job Type: Full-time
Location: Greater Washington DC area (onsite requirement 3-4x per week)
The Role
We’re hiring a Forward Deployed Engineer to build and deploy applied AI systems supporting high-impact U.S. Government missions. This role combines software engineering, AI development, and infrastructure deployment in high-assurance, mission-critical environments.
You'll work directly with government partners to translate operational needs into reliable technical solutions, taking ownership from initial prototyping through production deployment and ongoing improvement.
What You'll Work On
- Build and deploy agentic AI systems supporting U.S. Government missions, from prototype to production.
- Develop and integrate LLM applications, including multi-agent systems, tool-using agents, RAG workflows, and human-in-the-loop autonomy.
- Work directly with government partners to understand mission requirements, integrate solutions, and troubleshoot technical challenges.
- Build and maintain Python applications, APIs, data pipelines, and integrations with existing government systems.
- Develop infrastructure for model inference, experimentation, evaluation, and deployment across AI platforms.
- Deploy and maintain AI applications across cloud, on-premises, or restricted environments, ensuring reliability and security.
- Collaborate with engineering and research teams to improve model performance and translate mission feedback into product improvements.
- Own technical solutions throughout their lifecycle, including development, deployment, monitoring, and iteration.
What We're Looking For
- Strong Python engineering skills with hands-on experience building and maintaining production software.
- Experience developing LLM applications, AI agents, or agentic workflows.
- Experience with APIs, data pipelines, system integrations, and backend development.
- Familiarity with cloud infrastructure, containerization, and deployment technologies such as Docker, Kubernetes, or CI/CD pipelines.
- Experience with ML infrastructure, model evaluation, or AI experimentation workflows.
- Ability to independently solve technical problems and deliver solutions in ambiguous, customer-facing environments.
- Strong communication skills and ability to translate operational requirements into working technical systems.
- Comfortable operating in high-security, high-reliability, and mission-critical environments.
Preferred Qualifications
- Active U.S. security clearance or eligibility and willingness to obtain one.
- Experience supporting U.S. Government, defense, intelligence, or national security missions.
- Experience deploying software in restricted, air-gapped, or government cloud environments.
- Familiarity with RL workflows, simulation environments, or advanced AI evaluation techniques.
- Prior experience at a startup, defense organization, Department of War, or government-focused technology company.
- Background operating in fast-moving startup environments, mission-critical government settings, or both.
Compensation & Benefits Notice
The national pay range for this full-time position is base salary of $150,000 –$200,000. All employees are eligible for equity compensation, and employees may also receive performance-based bonuses, dependent on role and subject to company policies. micro1 provides a comprehensive benefits package, including up to 100% reimbursement for health-insurance premiums, paid time off, a 401(K) plan with a company match, and additional benefits designed to support a high-performing, remote-first workforce.
micro1 is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, veteran status, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation during the application process, reach out to support@micro1.ai.
Our hiring process utilizes artificial intelligence tools to assist in candidate screening and assessment. Our AI tools are designed to complement, not replace, human decision-making.
Prep for this interview
5 questions likely to come up at micro1, each matched to a line in this posting.
- 1LLMs, RAG and agents
They wrote: “2. Experience developing LLM applications, AI agents, or agentic workflows.”
A customer wants a chatbot over their internal documents. How do you build it?
How to answer
Why they ask: RAG is the most common first project. They want the whole path, from messy files to answers people trust.
- Collect and clean the documents; handle permissions per user
- Split them into chunks, embed, and store with source links
- Retrieve, then answer with citations; say “I don't know” when retrieval is weak
- Build a test set of real questions with expected answers before launch
Tip: Spend more time on the data and on testing than on the model.
- 2Integrations and APIs
They wrote: “4. Build and maintain Python applications, APIs, data pipelines, and integrations with existing government systems.”
Walk me through connecting our product to a customer's internal system that has no documentation.
How to answer
Why they ask: This happens weekly in the job. They want a calm, step-by-step method.
- Find who owns the system and get read access to a test environment
- Watch real traffic or logs to learn the data shapes
- Build a thin adapter with logging around every call
- Agree on how failures get reported, and who fixes what
Tip: Talk about finding the owner of the system first. The code is the easy part.
- 3Cloud, deployment and security
They wrote: “6. Deploy and maintain AI applications across cloud, on-premises, or restricted environments, ensuring reliability and security.”
A bank wants our product inside their own cloud account, with no data leaving it. How do you approach it?
How to answer
Why they ask: Self-hosted and private-cloud deployments are common in regulated industries.
- Learn their rules early: network limits, approved services, review process
- Package the product so it installs the same way each time (containers, Terraform)
- Plan how you'll get logs and updates without touching their data
- Run a test install with them before the real one
Tip: Talk about the conversation with their security team as much as the setup.
- 4Coding
They wrote: “1. Strong Python engineering skills with hands-on experience building and maintaining production software.”
Here's a CSV export from a customer. Write a script that cleans it and loads it into our API.
How to answer
Why they ask: Very close to real FDE work. They watch how you handle bad rows and failures.
- Read with a proper CSV parser; check the header first
- Validate each row and collect errors, not crash on the first one
- Send in batches with retries and a clear report at the end
- Write one or two quick tests for the parsing
Tip: Talk through edge cases out loud: encodings, missing fields, duplicates, rate limits.
- 5Data and SQL
They wrote: “3. Experience with APIs, data pipelines, system integrations, and backend development.”
How would you design a daily pipeline that feeds customer data into our product?
How to answer
Why they ask: Tests basic data engineering: scheduling, failures, re-runs and monitoring.
- Extract with timestamps or change tracking, not full dumps, when possible
- Transform in steps you can re-run without creating duplicates
- Validate row counts and key fields before loading
- Alert a person on failure, with enough detail to fix it
Tip: Make re-running safe (same input, same result) and say how you'd know it failed.
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