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About This Role
We are seeking a Security Research Engineer to operate as a hybrid Forward Deployed Engineer and offensive security researcher. You'll be on the front lines of customer engagements — using our open source tool Apex to run pentests, curate and present findings, and stand up our platform inside customer environments. In parallel, you'll drive original offensive and open source security research, and feed everything you learn in the field back into the product so Pensar keeps getting sharper as a pentesting platform.
This role is customer\-facing by design. The ideal candidate is equally comfortable in a terminal popping shells with Apex, on a Zoom with a CISO walking through findings, and in a design review arguing for the next product capability.
Key ResponsibilitiesCustomer Engagements \& Forward Deployed Work* Run end\-to\-end pentest engagements for customers using Apex, our open source offensive security tool
- Curate, triage, and contextualize findings for customer audiences ranging from engineers to executives
- Deliver clear, prioritized write\-ups and walk customers through results, exploitation paths, and remediation
- Set up and configure the Pensar platform inside customer environments, including integrations and workflows
- Act as a trusted technical partner for customers throughout onboarding, engagements, and ongoing usage
- Travel to customer sites as needed for kickoffs, readouts, and on\-site testing
Offensive Security Research* Conduct original offensive security research across web, cloud, infrastructure, and AI/LLM attack surfaces
- Develop new exploitation techniques, payloads, and tooling that extend Apex's capabilities
- Build automated testing methodologies for emerging vulnerability classes and attacker tradecraft
- Track the evolving threat landscape and translate it into concrete detections and capabilities
Open Source Security Research* Lead vulnerability research across high\-impact open source projects and ecosystems
- Verify findings, build proof\-of\-concept exploits, and coordinate responsible disclosure with maintainers
- Contribute patches, advisories, and tooling back to the open source community
- Grow Pensar's reputation in the security research community through publications, talks, and contributions
Product Feedback \& Pentesting Roadmap* Translate firsthand engagement experience into concrete recommendations for the product roadmap
- Partner with engineering and product on capabilities, UX, and automation that make pentesting faster and more reliable
- Participate in architecture and design reviews with a focus on the pentester's workflow
- Help shape Apex's direction as an open source project alongside the internal platform
Compensation* Base salary: $120,000 – $175,000 per year, depending on experience
- Meaningful equity in an early\-stage offensive security company
- Final offers calibrated to depth of offensive security experience, the breadth of your research record, and the level you join at
Reports To
CEO / CTO
*We are an equal opportunity employer committed to diversity and inclusion. We welcome applications from all qualified candidates regardless of race, gender, age, religion, sexual orientation, or disability status.*
Requirements
- 5\+ years of experience in offensive security, pentesting, red teaming, or vulnerability research
- Strong programming skills in multiple languages (Python, Go, JavaScript, C/C\+\+)
- Deep, hands\-on understanding of modern vulnerability classes across web, cloud, and infrastructure
- Proven track record of running pentest engagements end\-to\-end and delivering findings to customers
- Excellent customer\-facing communication skills — comfortable presenting to both engineers and executives
- Experience contributing to or maintaining open source security tooling
- Bachelor's degree in Computer Science, Cybersecurity, or related field, or equivalent experience
Preferred Qualifications* Experience with AI/LLM\-assisted offensive security or building security automation on top of LLMs
- Prior Forward Deployed Engineer, solutions engineering, or consulting experience at a security or developer tools company
- Security certifications (OSCP, OSCE, OSWE, GXPN, or equivalent)
- Public security research, CVEs, conference talks, or notable open source contributions
- Experience with cloud security (AWS, GCP, Azure) and containerized environments
- Familiarity with compliance frameworks (SOC 2, ISO 27001, PCI DSS) as they relate to pentesting
Benefits
- Comprehensive health, dental, and vision insurance
- Direct ownership of customer engagements and offensive research at an early\-stage security company
- Professional development budget for conferences, training, and certifications
- Support for publishing research and presenting at industry conferences
- Direct, visible impact on both our open source tooling and commercial platform
Salary Context
This $120K-$175K range is below the median for Research Engineer roles in our dataset (median: $185K across 51 roles with salary data).
View full Research Engineer salary data →Role Details
About This Role
Research Engineers bridge the gap between research and production. They implement papers, build experiment infrastructure, optimize training pipelines, and make research prototypes production-ready. They're the engineers who make research work at scale.
The role sits at a unique intersection. You need to understand the math well enough to implement novel architectures correctly, and you need the engineering chops to make them run efficiently on distributed systems. When a research scientist has a breakthrough idea, you're the person who turns it from a notebook prototype into a training pipeline that runs on 256 GPUs.
Across the 3,824 AI roles we're tracking, Research Engineer positions make up 2% of the market. At Pensar, this role fits into their broader AI and engineering organization.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
What the Work Looks Like
A typical week involves: implementing a new attention mechanism from a recent paper, profiling and optimizing a training pipeline that's bottlenecked on data loading, building evaluation infrastructure for a new benchmark, debugging distributed training issues across a GPU cluster, and pair-programming with a research scientist on their latest experiment. The work is deeply technical.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
Skills Required
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Experience with large-scale training infrastructure (FSDP, DeepSpeed, Megatron), GPU programming (CUDA, Triton), and the internals of ML frameworks (PyTorch internals, custom autograd functions) is what makes candidates stand out. The best research engineers can debug issues that span the full stack from GPU memory management to numerical precision to algorithmic correctness.
Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
Compensation Benchmarks
Research Engineer roles pay a median of $260,000 based on 401 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($147K) sits 43% below the category median. Disclosed range: $120K to $175K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Pensar AI Hiring
Pensar has 1 open AI role right now. They're hiring across Research Engineer. Based in New York, NY, US. Compensation range: $175K - $175K.
Location Context
AI roles in New York pay a median of $210,000 across 2,448 tracked positions. That's 5% above the national median.
Career Path
Common paths into Research Engineer roles include Software Engineer, ML Engineer, Research Intern.
From here, career progression typically leads toward Senior Research Engineer, Research Scientist, ML Architect.
This is one of the best entry points into AI research without a PhD. Build a strong engineering portfolio with ML projects, contribute to open-source ML frameworks, and demonstrate that you can implement complex ideas correctly and efficiently. The transition to Research Scientist is possible with published first-author work, which some research engineer roles support.
What to Expect in Interviews
Technical screens test both engineering skill and research understanding. Expect coding rounds with performance-critical implementations (GPU optimization, efficient data loading). Be prepared to discuss papers relevant to the team's research area and explain how you'd implement key ideas. System design questions focus on training infrastructure: distributed training, experiment tracking, and compute resource management.
When evaluating opportunities: Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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