Reinforcement Learning & Multi-Agent Autonomy Researcher

About Precision Federal

Precision Federal is a Christian-owned small business building AI and software systems for federal agencies through the SBIR program. The founder is a Christian, and the company is run on Christian principles: every number we publish is real, every claim we make is verifiable, and the work is done with excellence as service to God. Every proposal we submit is backed by working prototypes built before we ask for a dollar.

How this role works

1. You join the bench. We name you (with a short bio) as a proposed consultant on proposals that match this role. Consent costs you nothing and commits you to nothing until an award funds.
2. A contract is awarded. Federal SBIR awards fund the team named in the proposal. Your engagement begins, at an hourly rate agreed before you were ever named.
3. You work remote, paid from the contract. ~40–80 hrs per funded award — and if no award ever funds, you owe nothing and lose nothing.

What you'll do

• Review our RL and multi-agent designs — reward, environment, baselines, seeds and evaluation protocol
• Say where a simpler planner or a scripted behaviour would win on the same task
• Assess sim-to-real transfer, safety constraints and human-on-the-loop integration
• Point us at the benchmark, environment or published baseline a reviewer will expect us to have beaten

Required qualifications

• Direct research or engineering experience with reinforcement learning or multi-agent systems, with published or deployed results
• Working knowledge of PPO, SAC, MARL frameworks, and standard environments and benchmarks
• U.S. citizen or lawful permanent resident (U.S. person)
• Willing to be named on a federal proposal with a one-page resume, and to sign a short letter of commitment

Preferred qualifications

• Army, DARPA, AFRL or ONR autonomy program experience
• Robotics or ROS 2 background — ground, air or maritime platforms
• Peer-reviewed publication record

Compensation & structure

1099 consulting engagement. $60–$100 per hour, confirmed in writing before you are named on any proposal. Hours are funded by federal contract awards — engagements on this bench have ranged from roughly 130 to 710 hours across a period of performance, fully remote. Work and pay begin only if the contract is awarded — no award, no hours, no obligation.

Apply — Reinforcement Learning & Multi-Agent Autonomy Researcher

Every application is read personally by the founder. We respond to every serious one.

First name *

Last name *

Email *

Phone

City & state

Years of relevant experience * Select Under 5 5–10 10–15 15–20 20+

Work authorization * Select U.S. citizen U.S. lawful permanent resident Other

Security clearance Select (optional) None Prior (inactive) Active

Military service Select (optional) None Veteran Retired military Currently serving (Guard/Reserve)

LinkedIn or portfolio link

Resume (PDF or Word) *

How did you hear about us?

Five questions

These are the substance of the application. Answer from your own experience — a few sentences each is plenty, and “this never came up” is a real answer we want.

1. How often did a reinforcement-learning result that depended on one lucky seed or a reward the environment let it game come up in your work, and roughly what did it cost — in hours, in people, or in rework? *

2. What tool or process does your organization use today for reinforcement learning and multi-agent autonomy, and where does it fall short? *

3. If a better way to handle reinforcement learning and multi-agent autonomy existed, which office or role would decide whether to adopt it? *

4. Do you have any role in deciding what tools or systems your organization adopts — approving, evaluating, or recommending a purchase? *

5. Do you own, or hold signing authority for, a company or consultancy of your own? *

May we name you, by name and title, as a proposed consultant on our team in federal proposals, and quote your answers above? * Please choose… Yes No

Submit application

Questions before applying? contact@precisionfederal.com · See all open roles