AI, Machine Learning and Data Science Recruiters
No postings. No stacks of resumes. No chasing. You interview a short list.
No upfront fee. You pay only if you hire.
Need someone now? Call 305-306-4829.
This is what we do all day: recruit technology and IT professionals.
Find working specialists across AI, Machine Learning and Data Science, qualified for the scope you need.
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.
Every title we cover
- Machine Learning Engineer
- Senior / Staff ML Engineer
- AI Engineer
- LLM / Generative AI Engineer
- AI Agent Engineer
- MLOps Engineer
- ML Platform / Infrastructure Engineer
- Data Scientist
- Senior Data Scientist
- Decision Scientist
- Applied Scientist
- Research Scientist
- Research Engineer
- Computer Vision Engineer
- NLP Engineer
- Recommendation Systems Engineer
- Quantitative Developer / Researcher
- Head of AI / VP of AI
- Head of Data Science
- Chief AI Officer
- Senior Machine Learning Engineer
- Staff Machine Learning Engineer
- Principal Machine Learning Engineer
- Lead Data Scientist
- Machine Learning Engineering Manager
- Computer Vision Machine Learning Engineer
- NLP Machine Learning Engineer
- Recommendation Systems Machine Learning Engineer
- Fintech AI Engineer
- SaaS AI Engineer
- Construction Tech AI Engineer
The artificial intelligence and data science discipline focuses on constructing scalable machine learning systems and deriving actionable insights from complex datasets. Teams build infrastructure for production models while conducting research into novel algorithms. Engineers in this domain work across the entire lifecycle from data ingestion to model deployment and monitoring. Success requires deep mathematical foundations combined with rigorous software engineering practices.
Our hiring focus covers specialized roles ranging from machine learning engineers who optimize model performance to research scientists exploring frontier architectures. We prioritize talent capable of bridging the gap between raw data and product impact. Whether building generative models or recommendation systems, these roles demand high technical proficiency to work through the rapid evolution of artificial intelligence frameworks and platform requirements.
What we screen for
Algorithmic Proficiency Check
We evaluate deep familiarity with fundamental machine learning algorithms including regression, classification, clustering, and neural network architectures. Candidates must demonstrate an ability to select the appropriate model for specific business problems while justifying their design choices. Understanding bias, variance, and feature engineering techniques is vital. Proficiency with core Python libraries and high level mathematical concepts remains a primary indicator of competence.
Model Deployment Competency
This check verifies experience transitioning models from research environments to production pipelines. We assess knowledge of containerization tools, API design, and serving infrastructure required to maintain low latency. Practical exposure to handling versioning, drift detection, and data pipelines is essential. Candidates need to explain how they optimize compute resources to balance cost against performance in high volume environments.
Generative AI Understanding
We investigate technical depth regarding large language models, transformer architectures, and fine tuning methodologies. Candidates should discuss their practical experience with prompt engineering, vector databases, and retrieval augmented generation workflows. Understanding the limitations, ethical implications, and safety concerns of these models is as important as the ability to deploy them effectively within large scale software applications.
Data Architecture Evaluation
This assessment reviews expertise in designing data collection, storage, and cleaning processes that feed effective models. We look for proficiency in distributed computing frameworks and database technologies commonly used for large scale analytics. Understanding the trade-offs between batch and streaming architectures is crucial. Candidates must show evidence of their capacity to manage data quality issues across complex streams.
Research Implementation Capability
We probe for experience translating complex research papers into functional software prototypes. Candidates should describe how they replicate experiments, interpret academic literature, and iterate based on empirical evidence. This involves debugging mathematical logic and adapting architectures to suit specific technical constraints. Demonstrating a rigorous approach to testing, performance validation, and documentation is essential for success in this domain.
System Design Skills
Our final check focuses on the ability to design end to end AI systems that are modular, scalable, and maintainable. We evaluate knowledge of microservices, event driven architectures, and cloud native services. Candidates must articulate how they handle data flow, model updates, and system monitoring in production. The goal is to identify engineers who prioritize long term system health.
Related skills and ecosystems we cover
- Snowflake
- Databricks
- Spark
- Kafka
- dbt
- Airflow
- BigQuery
- Power BI
- Tableau
- PyTorch
- LLM stack (model APIs, LangChain / LlamaIndex, vector databases)
Where we find them
We source talent by identifying active contributors to open source machine learning frameworks and repositories that advance the state of the art in artificial intelligence. We seek individuals who publish practical insights on advanced modeling or infrastructure challenges. By observing how developers contribute to complex ecosystems, we locate experts who are deeply embedded in the evolving machine learning community.
Beyond digital repositories, we connect with professionals who build specialized infrastructure at companies known for high performance modeling and data intensive applications. We target engineers at firms that prioritize technical excellence in research and production environments. This strategy captures talent with rigorous habits who are accustomed to shipping models that drive real time decision making and performance.
Dedicated pages in this family
- Machine Learning Engineer
- AI Engineer
- MLOps Engineer
- Data Scientist
- Research Scientist / Applied Scientist
- Computer Vision Engineer
- Quantitative Developer / Researcher
- Head of AI
- Senior / Staff ML Engineer
- LLM / Generative AI Engineer
- AI Agent Engineer
- ML Platform / Infrastructure Engineer
- Senior Data Scientist
- Decision Scientist
- Applied Scientist
- Research Scientist
- Research Engineer
- NLP Engineer
- Recommendation Systems Engineer
- Head of Data Science
- Chief AI Officer
- Senior Machine Learning Engineer
- Staff Machine Learning Engineer
- Principal Machine Learning Engineer
- Lead Data Scientist
- Machine Learning Engineering Manager
- Computer Vision Machine Learning Engineer
- NLP Machine Learning Engineer
- Recommendation Systems Machine Learning Engineer
- Fintech AI Engineer
- SaaS AI Engineer
- Construction Tech AI Engineer
How a AI, Machine Learning and Data Science search runs
Our engagement process begins with a deep dive into the candidate technical portfolio to understand their past architectural contributions and research successes. We focus on specific problem solving outcomes rather than just tool lists. This initial stage allows us to assess the depth of technical competence while ensuring alignment with our hiring objectives and high performance engineering culture.
Following initial technical reviews, the process transitions to practical discussions regarding engineering practices, testing strategies, and system design philosophies. We emphasize collaborative assessment of real world challenges to understand how candidates approach ambiguity and scaling concerns. This multi stage interview approach ensures we evaluate long term technical impact and cultural fit for our specific engineering team needs.
What happens next
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.
Questions about this search
When will we see a shortlist?
We agree the search plan after checking your requirements for algorithmic proficiency check and model deployment competency, rather than promising a date before the brief is clear. We evaluate deep familiarity with fundamental machine learning algorithms including regression, classification, clustering, and neural network architectures.
What if a candidate gets a counteroffer?
We ask what would make someone leave work involving algorithmic proficiency check, and what could make them stay. Before you invest in interviews, we discuss whether your offer changes the problem they want to leave, including the responsibility for system design skills.
Should this role be remote or on site?
Experience with algorithmic proficiency check and model deployment competency does not settle the location question. Tell us what access and supervision the work needs, including who owns decisions about system design skills, so we approach people who can accept the arrangement.
How will you check fit before we interview?
We evaluate deep familiarity with fundamental machine learning algorithms including regression, classification, clustering, and neural network architectures. Candidates must demonstrate an ability to select the appropriate model for specific business problems while justifying their design choices. This check verifies experience transitioning models from research environments to production pipelines.
How does the fee work?
No upfront fee. You pay only if you hire. Agree the fee and the responsibilities covering algorithmic proficiency check and model deployment competency in the recruiting agreement before the search proceeds.
What if the hire does not work out?
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. Make sure the agreed scope includes what you expect of system design skills, rather than leaving that discussion until after the start.
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.
Related roles and stacks
How 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
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.