Research Engineer Recruiters
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Need someone now? Call 305-306-4829.
This is what we do all day: recruit technology and IT professionals.
When a research idea needs repeatable experiments, the engineer must make the implementation dependable without quietly changing what the experiment tests.
No upfront fee. You pay only if you hire.
No upfront fee. You pay only if you hire. We offer a candidate replacement guarantee on standard search agreements. The applicable period and exact terms are set out in the signed client recruiting agreement.
What happens next
- Do I pay upfront?
- No upfront fee. You pay only if you hire.
- Must I give you exclusivity?
- We do not promise or require exclusivity here. Any terms are in the recruiting agreement we refer you to.
- Will you reveal my company?
- Not without your explicit consent before we disclose anything.
- What if I do not hire?
- No hire, no fee.
- No upfront fee
- Pay only if you hire
- Technology only
- We reach engineers who aren't on job boards
- Replacement guarantee on standard searches
Need someone now? Call 305-306-4829.
What this hire really comes down to
When a research idea needs repeatable experiments, the engineer must make the implementation dependable without quietly changing what the experiment tests. Many managers treat these roles as pure research functions, which often results in notebooks that never ship. This role demands an engineer who prioritizes runtime stability and memory efficiency while maintaining parity with the underlying research math. The primary goal is to strip away technical debt inherent in early-stage prototyping to ensure the system scales under real user traffic.
Do not confuse this person with a Data Scientist or a standard Backend Engineer. A Data Scientist focuses on feature engineering and validation performance metrics. A Backend Engineer manages API endpoints and databases. The Research Engineer sits in the middle, translating complex tensor operations into optimized C++, CUDA, or high-performance Python. They handle the hardware utilization constraints and the specific nuances of hardware acceleration that standard developers rarely encounter. Your hire must demonstrate the ability to maintain the model's accuracy while reducing latency by an order of magnitude.
Expect the candidate to define the interface between the model and the application layer. They should know how to quantize weights or implement pruning without breaking the model's logic. If they cannot explain the trade-offs between training throughput and inference latency in your specific cloud environment, they are likely just a software engineer with an interest in machine learning. Focus the evaluation on their history of deploying models to production rather than their ability to replicate papers.
What we check before you see anyone
Kernel Optimization
Ask if they have written custom kernels or modified existing ones in PyTorch or TensorFlow. If they only use high-level APIs like Keras, they lack the low-level expertise to fix production bottlenecks.
Memory Management
Inquire about their strategies for handling OOM errors during inference. Look for specific examples of managing GPU memory fragmentation and how they adjusted batch sizes or input tiling to compensate.
Deployment Frameworks
Check their experience with Triton Inference Server, ONNX Runtime, or TensorRT. They should explain how these tools impact serialization and model conversion, not just how to install them.
Quantization Accuracy
Ask them to explain a time they quantized a model from FP32 to INT8 and how they measured the resulting accuracy drop. A good answer includes the validation method used to ensure the model still behaves correctly.
Where we find them
Look for candidates in organizations where model deployment is a constant requirement, such as search engine companies, computer vision start-ups, or robotics firms. You need people who have faced the frustration of models that work on a local machine but fail in production. Avoid individuals whose background is strictly in academic publishing without any evidence of code review or CI/CD integration.
Motivation for these individuals is usually solving hard performance problems. They gravitate toward roles where they have hardware access and control over the full stack. If you offer a role that restricts them to data cleaning or model training without exposure to the inference path, they will likely lose interest. Look for those who enjoy the engineering rigor of software combined with the iterative nature of machine learning development.
Mistakes we see
Hiring based solely on publication history at conferences like NeurIPS, which confirms research potential but often ignores practical coding discipline and production experience.
Expecting the engineer to perform data science tasks like feature engineering or data labelling, which pulls them away from the infrastructure work they were actually hired to perform.
Overlooking the need for version control discipline in machine learning, resulting in unrepeatable model builds and fragmented documentation across different teams.
The search in three steps
Agree the brief
During intake, define the specific hardware constraints. Are you running on edge devices, proprietary server racks, or cloud GPU instances? This dictates the type of optimization expertise required.
Find and speak with people
Conduct a headhunt targeting engineers with systems backgrounds. Focus on those who have spent time in C++ development before moving into deep learning or those who specialized in computer systems at the graduate level.
Introduce the people who fit
Evaluate them with a live coding session that focuses on debugging a slow model inference pipeline. Provide a model that is memory-inefficient and ask them to optimize it in real time while justifying their code changes.
Questions about this search
How should I handle counteroffers for this specialty?
This market is tight, and candidates often receive multiple offers. If they cite a counteroffer, confirm if the competing firm offers the same hardware access and infrastructure freedom. If they do not, point that out as a differentiator.
Does this role require an advanced academic degree?
A degree is helpful but not mandatory. What matters is the ability to read and implement complex white papers. Verify their work via public code repositories rather than just credentials.
Is remote work feasible for this role?
Remote work is possible if you can provide secure, reliable access to the required computing clusters. The risk is reduced oversight during high-intensity debugging sessions that might require physical presence.
How do I ensure they are technically fit?
Require a code sample from their previous work that demonstrates model deployment. Review their handling of edge cases, error logging, and resource management within that specific piece of software.
What is the standard payment structure?
Compensation includes base salary and equity. Avoid bonus structures tied to model accuracy, as research outcomes are unpredictable. Focus on performance-based incentives for system uptime or latency reduction.
No upfront fee. You pay only if you hire. We offer a candidate replacement guarantee on standard search agreements. The applicable period and exact terms are set out in the signed client recruiting agreement.
What happens next
- Do I pay upfront?
- No upfront fee. You pay only if you hire.
- Must I give you exclusivity?
- We do not promise or require exclusivity here. Any terms are in the recruiting agreement we refer you to.
- Will you reveal my company?
- Not without your explicit consent before we disclose anything.
- What if I do not hire?
- No hire, no fee.
Pay snapshot
Research Engineer
$140,300
Median as published, released May 15, 2026. Matched to Computer and Information Research Scientists (SOC 15-1221).
BLS reports Computer and Information Research Scientists as a broader occupation. It does not separate Research Engineer, technology stack, seniority, certifications or employer sector. These figures are not a title-specific offer range.
Source: BLS OEWS May 2025.
See the salary guideHow a search runs
The Six-Step Search
- 1Search Calibration
A call about the work. You get a written search profile.
- 2Market Mapping
We identify the employer backgrounds that fit. You see where we will look.
- 3Professional Contact Intelligence
We identify individual people, not job board applicants.
- 4Persistent Personalized Outreach
Personal outreach and direct follow-through. You get search updates.
- 5Live Candidate Qualification
A recruiter checks stack, scope and motivation in conversation.
- 6Candidate Intelligence and Process Support
You receive a written brief on every person you meet.
Working with Technology Headhunters
When is the recruiting fee due?
There is no upfront fee for our contingency search. You pay only if you hire someone we present, under the terms of the signed agreement.
How long does it take to see a short list?
We first need to agree the opening and qualify people against it. Availability and the interview requirements affect the search; we do not promise a fixed deadline before reviewing the work.
What are the replacement terms?
We offer a candidate replacement guarantee on standard search agreements. The applicable period and exact terms are set out in the signed client recruiting agreement.
What don't you do in a permanent search?
We do not bill by the hour for permanent search or charge a retainer unless one is agreed. We do not treat reposting your job advertisement as headhunting.