Resumes show claims. We show proof. Reinforcement Learning Engineers assessed on reward shaping architecture, policy optimization algorithms, and sim-to-real transfer strategy — so you interview candidates, not question marks.
No credit card required.
Talent Marketplaces give you a resume. We give you the source code.
Candidate A
Software Engineer
Self Reported
2024
Experience
5 years React / Frontend Development
No portfolio links
Previous Roles
X-Corp
Tech Solutions Inc.
Education
B.S. Computer Science — State University
• UNVERIFIED CLAIM
Verified ProofedVerified Engineer
ConnectDevs Intelligence Dossier
SAM TECH SCORE
98/100
CODE QUALITY
A+
TECHNICAL INTERVIEW HIGHLIGHTS
Play Recorded Proof
const solveHardProblem = (data) => {
return data.reduce((acc, val) => {
// Verified optimal O(n) solution
return { ...acc, [val.id]: val.performance };
}, {});
};DECISION-READY DATA
You set the criteria. Scout ranked the matches. Now choose who's worth your time.
7 Years
89%
Match Score
FinTech Global
Georgia Institute of Technology
B.S. Computer Science
2012 - 2016
Alex Mercer
Senior Mobile Engineer
2021 – Present
7 Years
89%
Match Score
FinTech Global
Georgia Institute of Technology
B.S. Computer Science
2012 - 2016
Sarah Chen
Senior Mobile Engineer
2021 – Present
7 Years
89%
Match Score
FinTech Global
Georgia Institute of Technology
B.S. Computer Science
2012 - 2016
David Rodriguez
Senior Mobile Engineer
2021 – Present
We analyze thousands of placements to give you real-time salary data for every experience level.
Role: Junior Reinforcement Learning Engineer
0-2 Years
Entry-level profile with a strong foundation in Markov decision processes, basic policy gradients, and simulation environments.
REQUIREMENTS
Degree in Computer Science or equivalent practical training.
Hands-on experience implementing RL algorithms in OpenAI Gym or similar environments.
Familiarity with policy gradient methods and basic reward function design.
Junior Developer Hourly Rate
Average Yearly Salary ~$90k /yr
Market
Signal
Entry Baseline
Steady demand for junior RL talent as robotics and autonomous systems expand across industries.
Role: Mid Reinforcement Learning Engineer
2-5 Years
Mid-level profile with proven expertise in advanced policy optimization, reward engineering, and distributed training.
REQUIREMENTS
Degree in Computer Science or equivalent practical training.
Demonstrated ability to implement and tune PPO and SAC algorithms for complex control tasks.
Experience designing reward functions that avoid common failure modes like reward hacking.
Mid Developer Hourly Rate
Average Yearly Salary ~$111k /yr
Market
Signal
Algorithm Tuning
Growing demand for engineers who can stabilize training in complex, high-dimensional environments.
Role: Senior Reinforcement Learning Engineer
5+ Years
Senior profile with deep mastery of sim-to-real transfer, multi-agent systems, and production robotics deployment.
REQUIREMENTS
Degree in Computer Science or equivalent practical training.
Proven track record deploying RL policies from simulation to physical systems with domain randomization.
Experience leading RL projects for robotics, autonomous vehicles, or industrial automation.
Senior Developer Hourly Rate
Average Yearly Salary ~$132k /yr
Market
Signal
Sim-to-Real Expert
Senior RL engineers with production robotics experience commanding premium rates in the autonomous systems market.
Traditional agencies take weeks. Our Intelligence Engine runs in parallel to deliver decision-ready profiles in real-time.
Hour 0
Signal Ingestion
You define the stack. Scout maps intent signals across 550M+ profiles.
Hours 2–24
Parallel Processing
Scout scans candidate profiles while Pilot launches multi-channel outreach. The system works asynchronously while you sleep.
Scout
Mass Ingestion
Parsing your role. Scanning 800M+ engineers. Surfacing matches—live results.
Pilot
Engagement
Sending interview invites. Tracking responses. Moving candidates to SAM—pipeline
SAM
Validation
Conducting interviews. Evaluating skills. Compiling decision-ready report now
const score = validate(dev);
if (score > 0.92) dispatch(shortlist);
Hour 48
You Receive Your Shortlist
3 Decision-Ready Profiles delivered to your dashboard.
STATUS: READY
Intelligent Shortlist
Candidates Found
1,204
Validated Skills
Reinforcement Learning, Node, Go
Top Matches
03
Marketplaces show you profiles. We show you capability.
The Problem
When you browse a talent marketplace, you are guessing. You see a resume that claims '5 Years Reinforcement Learning,' but you don't know:
Can they design reward shaping mechanisms that don't inadvertently incentivize dangerous edge-case behaviors?
Have they successfully bridged the sim-to-real gap in production systems, not just academic projects?
Can they diagnose and resolve catastrophic policy collapse during prolonged training runs?
The Solution
ConnectDevs removes the guesswork. We don't just send profiles; we send Structured Intelligence. Every candidate is interviewed by SAM against the specific Reinforcement Learning challenges you care about. You don't guess if they are good. You know.
Unverified Claim
Reinforcement Learning Developer
5 Years Experience
Verified Proof
CODE CHALLENGE
Solve a problem using algorithms
SAM INTERVIEW
Discuss alternative approaches and their trade-offs
TECH SCORE
98/100 Algorithm Score
GITHUB AUDIT
Active Open Source Contributor
For Reinforcement Learning Engineers, we specifically test for reward shaping architecture, policy optimization algorithms, and sim-to-real transfer strategy. You get the raw data before you even interview.
Agencies charge a markup every hour. We charge a flat platform fee. You keep the savings.
Number of developers
3 Devs
1
10
Role seniority
Base Salary: $120,000
Includes 35%
Zero Markup
Estimated Yearly Savings
If SAM doesn't surface interview-ready candidates your LinkedIn search missed—you pay nothing.
FLEXIBLE
0%
Zero Markup
We don't inflate developer rates or take recruitment fees.
Cancel Anytime
No lock-ins. No notice required. Keep your data.
Average time-to-shortlist
Global Talent Network
Everything you need to know about sourcing, assessing, and hiring top Reinforcement Learning Engineers through our platform.
SAM's technical interview presents candidates with sparse reward environments and asks them to design intermediate signals. They must demonstrate awareness of reward hacking risks and mitigation strategies. You receive a scored report showing their reward engineering capabilities.
Senior RL engineers command average salaries around $132,000 annually. Traditional agencies extract 20-35% placement fees. ConnectDevs operates on a flat $69/mo subscription with zero markup, significantly reducing total hiring cost.
The Scout agent searches 800M+ public profiles for precise policy optimization and simulation environment signals. This delivers a targeted shortlist in days rather than the weeks typical of manual sourcing.
SAM interrogates candidates on domain randomization techniques, partial observability handling, and transfer learning strategies. The structured evaluation reveals whether their simulation experience translates to real-world deployment.
SAM's evaluation specifically tests PPO and SAC implementation depth, including entropy regularization tuning and learning rate scheduling. The assessment reveals whether candidates can stabilize training in complex environments.
Every ConnectDevs engagement provides raw assessment data upfront, including competency scores and recorded technical interviews. Audit the data before you invest interview time to minimize the risk of a costly mis-hire.