{"id":54155,"url":"https://alion.io/job/bugcrowd-reinforcement-learning-engineer-cybersecurity","title":"Reinforcement Learning Engineer","company":{"id":5414,"name":"Bugcrowd","domain":"bugcrowd.com","url":"https://alion.io/company/bugcrowd","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":176400,"max":242550,"currency":"USD","period":"year","gross":null,"usd_annual":242550},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"Cohere SDK","optional":false},{"name":"Docker","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Linux","optional":false},{"name":"LLM","optional":false},{"name":"OpenAI","optional":false},{"name":"Python","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Rust","optional":false}],"status":"live","first_seen_at":"2026-07-15T19:08:27Z","employer_posted_date":"2026-09-28","last_verified_at":"2026-10-16T12:14:50Z","board_verified":true,"closed_at":null,"days_open":92,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":92},"description":"Founded in 2012, Bugcrowd is the preemptive security platform that unifies exposure discovery and assessment, offensive testing, and intelligence shaped by AI and human insight to help organizations avoid, discover, and validate real-world risk. Bugcrowd helps security teams move faster by identifying the exposures that matter most so they can act first and stay ahead of attackers. By combining the power of humans and AI, teams can preempt attack paths and prevent breaches. Based in San Francisco and New Hampshire, Bugcrowd is supported by General Catalyst, Rally Ventures, Costanoa Ventures, and others. Visit www.bugcrowd.com.\nJob Summary\nThe Bugcrowd RL and Reasoning Team focuses on pushing the boundaries of autonomous cybersecurity by building authentic, verifiable reinforcement learning environments for world-leading foundational AI companies. As a Reinforcement Learning Engineer specializing in Reinforcement Learning from Verifiable Rewards (RLVR), you will design and scale automated verification pipelines that transform real-world software vulnerabilities into deterministic reward functions. In this role, you will bridge the gap between low-level security analysis and modern LLM reasoning models, engineering environments where AI agents learn to discover, exploit, and remediate software vulnerabilities with mathematical certainty. Instead of relying on subjective human feedback, your work directly powers the rigorous, verifiable reward signals that teach next-generation frontier AI models how to master complex cybersecurity domain logic. You will work at the intersection of fuzzing, dynamic program analysis, system exploitation, and scalable ML infrastructure to shape the safety and offensive/defensive capabilities of future artificial intelligence. \nEssential Duties and Responsibilities \nDesign, build, and deploy high-throughput RLVR (Reinforcement Learning from Verifiable Rewards) environments that evaluate LLM action sequences against deterministic execution outcomes.\nDevelop automated test harnesses, sandboxes, and verification engines that convert complex vulnerability research (e.g., memory corruption, web security, logic bugs) into binary pass/fail reward signals.\nIntegrate Bugcrowd’s Mayhem automated analysis platform and real-world vulnerability feeds into continuous, scalable RL environment generation pipelines.\nArchitect safe, isolated, and highly reproducible execution environments (using Docker, BuildKit, or Nix) capable of running thousands of simultaneous agent-driven exploitation and patching trajectories.\nCollaborate directly with researchers at frontier AI labs including Anthropic, OpenAI, and Cohere to define standard benchmark formats, observation spaces, and verifiable evaluation metrics for cybersecurity tasks.\nImplement precise telemetry, ground-truth verification algorithms, and trajectory logging to analyze agent reasoning paths and prevent reward hacking or false positives.\nBuild low-level instrumentation and debugging tools to monitor memory states, process executions, and network behaviors during agent interaction cycles.\nOptimize infrastructure performance and environment reset latency to support massive-scale parallel sampling and distributed RL training workflows.\nBenchmark and evaluate frontier AI model performance across diverse offensive and defensive security challenges, such as automated fuzzing, exploit payload generation, and patch validation.\nEducation, Experience, Knowledge, Skills, and Abilities\nUnderstanding of RL training workflows used by modern LLM systems, specifically execution-based feedback or Reinforcement Learning from Verifiable Rewards (RLVR).\nProficiency developing applications in Python and low-level systems programming in C, with Rust experience being a strong plus.\nSolid understanding of software vulnerabilities, binary exploitation, fuzzing methodologies, or program analysis.\nExperience with DevOps pipelines (e.g., GitHub Actions), reproducible builds (Docker, BuildKit, Nix), and comfort working with Linux systems and low-level debugging.\nExperience working with or building benchmark environments (e.g., CTFs, SWE-bench, security challenges, or execution sandboxes).\nPreferred Experience\nExperience designing custom reward functions, ground-truth verifiers, or automated grading engines for AI safety and reasoning models.\nBackground in low-level program analysis tools, sanitizers (e.g., ASan/MSan), compiler instrumentation, or automated exploit generation tools.\nProven track record of participating in or developing competitive cybersecurity benchmarks, CTFs, or open-source AI evaluation frameworks.\nWorking Conditions and Physical Requirements\nThe ideal candidate must be able to complete all physical requirements of the job with or without reasonable accommodation.\nSitting and / or standing - Must be able to remain in a stationary position 50% of the time\nCarrying and / or lifting - Must be able to carry / move laptop as needed throughout the work day.\nEnvironment - remote, work-from-home 100% of the time.\nPay Range Disclosure\nAt Bugcrowd, we strive for fairness, equality and to create an environment that allows our people to perform at their very best. Our compensation philosophy is to foster a collaborative community that rewards, attracts and retains the best possible talent. The provided salary details are based on US national averages and we retain the flexibility to tailor to the needs of the business.\nThe national estimate for the current base range for the position of $176,400 - $242,550.\nThis position may also be eligible to participate in a discretionary bonus program or commission plan, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.\nCulture\nAt Bugcrowd, we understand that diversity in the workplace is vital to a company’s success and growth. We strive to make sure that people are included and have a sense of being part of making Bugcrowd not only a great product but a great place to work.\nWe regularly hear from both customers and researchers that Bugcrowd feels like a family, and we strive to maintain that internally as well.\nOur team consists of a broad range of people: musicians, adventure sports junkies, nature lovers, parents, cereal enthusiasts, night owls, cyclists, artists-you get the point.\nAt Bugcrowd, we are solving security threats and vulnerabilities that are relevant to everyone, therefore we believe solving these problems takes all kinds of backgrounds. We value the perspectives and experiences people from underrepresented backgrounds bring.\nDisclaimer\nThis position has access to highly confidential, sensitive information relating to the technologies of Bugcrowd. It is essential that the applicant possess the requisite integrity to maintain the information in the strictest confidence.\nThe company is authorized to obtain background checks for employment purposes under state and federal law. Background checks will be conducted for positions that involve access to confidential or proprietary information (including trade secrets).\nBackground checks may include Social Security verification, prior employment verification, personal and professional references, educational verification, and criminal history. Applicants with conviction histories will not be excluded from consideration to the extent required by law.\nAny personal data you submit in connection with your application will be processed in compliance with Bugcrowd's Privacy Policy, which you may review here: https://www.bugcrowd.com/privacy.\nEqual Employment Opportunity:\nBugcrowd is EOE, Disability/Age Employer. \nIndividuals seeking employment at Bugcrowd are considered without regards to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, or sexual orientation. \nBugcrowd is committed to the full inclusion of all qualified individuals. In keeping with our commitment, Bugcrowd will take the steps to assure that people with disabilities are provided reasonable accommodations. Accordingly, if reasonable accommodation is required to fully participate in the job application or interview process, to perform the essential functions of the position, and/or to receive all other benefits and privileges of employment, please contact HR at ADA at bugcrowd.com.\nApply at: https://www.bugcrowd.com/about/careers/","description_format":"text","description_chars":8500,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":true,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Penetration Testing","Information Security","Application Security","Vulnerability Management"],"lifecycle":[{"event":"open","at":"2026-07-15T19:08:27Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"Filed H-1B visas in the last two years","filings_12m":0,"filings_prev_12m":1,"green_card_filings_12m":0,"median_offered_wage_usd":null,"route":null,"cap_exempt":false,"checked_at":"2026-10-15T11:24:14+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)"],"filings_for_role_12m":0}],"liveness":{"score":19,"band":"cold","label":"Long shot","p_open":1,"p_active":0.516,"p_room":0.36,"age_days":92,"expected_fill_days":55,"reasons":["conf:2","win:tail","crowd:"],"computed_at":"2026-10-16T05:45:00Z"},"pay":{"stated_usd_annual":242550,"is_top_pay":true},"html_url":"https://alion.io/job/bugcrowd-reinforcement-learning-engineer-cybersecurity","json_url":"https://alion.io/job/bugcrowd-reinforcement-learning-engineer-cybersecurity.json","meta":{"generated_at":"2026-10-16T16:24:57Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":912,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-17T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":5414},"rest":"https://alion.io/mcp/rest/get_company?id=5414"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fbugcrowd-reinforcement-learning-engineer-cybersecurity"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fbugcrowd-reinforcement-learning-engineer-cybersecurity"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fbugcrowd-reinforcement-learning-engineer-cybersecurity"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/bugcrowd-reinforcement-learning-engineer-cybersecurity\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fbugcrowd-reinforcement-learning-engineer-cybersecurity"}]}