Your machine learning recruitment specialists
Nexus IT Group’s specialized machine learning recruiters connect organizations with the ML engineers, applied scientists and MLOps practitioners who turn algorithms into production systems that move the business forward. Machine learning has gone from a research niche to a core capability across nearly every industry, yet the pool of seasoned ML talent has not kept pace with hiring demand. The best machine learning professionals are rarely active on job boards and they receive so many generic recruiter messages that most go ignored. Nexus takes a different approach to ML staffing, building relationships with passive candidates over time so that when we present an opportunity, it lands with credibility and fits what they actually want next in their careers.
Why machine learning talent is so hard to hire
Production ML requires an unusual blend of skills that spans statistics, software engineering, distributed systems and applied research. Companies need professionals who can move a model from a notebook into production, monitor for drift, retrain on fresh data and explain results to non-technical stakeholders. The rapid rise of generative AI, large language models and foundation models has pushed compensation higher while stretching candidate pools thinner across recommendation systems, computer vision, NLP and forecasting use cases. Our recruiting team has spent years cultivating networks across applied research labs, ML platform teams, MLOps groups along with computer vision and natural language processing shops. We can introduce you to engineers who have shipped fraud detection models, recommendation engines, vision pipelines and generative AI features that real customers actually use.
How Nexus solves machine learning hiring challenges
Hiring for ML roles takes nuance that generic IT recruiters often miss. A research-oriented scientist who publishes at NeurIPS may struggle in an environment that prizes shipping, while an engineer who excels at productionizing models may not be the right fit for an early-stage research team. Our machine learning recruiters take the time to understand where on this spectrum your team sits and which candidates will thrive there. We also coach candidates through the technical interview process, including take-home projects, system design rounds for ML platforms plus the modeling discussions that often catch applicants off guard.
Whether you’re a hiring manager with a tough machine learning role to fill or a practitioner thinking about your next move, we would love to chat about your needs and how Nexus can help. Similar to technology roles across industries, the machine learning sector is evolving quickly, and having an experienced professional on your side can help you successfully navigate this changing landscape. Even if you just want some insights into how our recruiters help connect talent with machine learning opportunities, we hope you’ll reach out so we can share our expertise and help your company or career continue to move forward.
Join the many companies that staff their roles with our machine learning talent
Nexus IT Group will quickly staff your open machine learning roles with high-quality committed candidates
81%
of our successful candidates are submitted within one week
92%
of our candidates will accept your offer
96%
of our candidates are employed with your firm after 12 months
We offer a range of services to meet your recruiting needs
Executive Search (Retained)
Direct Hire
Contract Staffing
Recruitment as a Service
We have filled a variety of machine learning roles
- AI Engineer
- AI/ML Engineer
- Applied Scientist
- Computer Vision Engineer
- Computer Vision Research Scientist
- Deep Learning Engineer
- Director of Machine Learning
- Engineering Manager, Machine Learning
- Generative AI Engineer
- Head of Machine Learning
- Large Language Model (LLM) Engineer
- Lead Machine Learning Engineer
- ML Product Manager
- MLOps Engineer
- Machine Learning Architect
- Machine Learning Engineer
- Machine Learning Infrastructure Engineer
- Machine Learning Platform Engineer
- Machine Learning Research Scientist
- Natural Language Processing (NLP) Engineer
- Principal Machine Learning Engineer
- Recommendation Systems Engineer
- Research Engineer
- Senior Machine Learning Engineer
- VP of Machine Learning
Recruit with Nexus IT Group in machine learning
What our machine learning clients have said about working with Nexus IT Group
The great thing about nexus IT group is that it has a smaller family feel, but they are able to deliver IT candidates better than the largest IT staffing suppliers that we work with. They have delivered high quality tech candidates for several years to our firm.
I’ve been in Talent Acquisition for 15 years and have used many IT staffing firms over the years. I highly recommend Nick and nexus IT group over any other IT staffing services. nexus’ commitment to quality and delivery is top tier. They take the time to understand your needs, provide thorough screening, and make sure that candidates have a positive on-boarding experience.
Working with nexus IT group has made my job much easier. Having a recruiting firm who specializes in IT and within my industry, and who understands our specific culture and hiring needs has been instrumental in our talent acquisition talent strategy.
Capital One has used nexus IT group’s recruitment services for a number of IT positions at varying levels, over the past 4 years. We’ve had a high success rate working with nexus IT group which is a testament to the caliber of their work and their internal sourcing and vetting practices.
I’ve dealt with a lot of hiring firms in my 18 years in the investment banking profession, and the majority of them have been awful to mediocre at best. Dealing with Nexus IT Group, on the other hand, was a breath of fresh air. After only one one opening, I was blown away by the quality and quantity of resumes I received. Nexus IT Group pays close attention to my demands and requirements. As a result, the individuals who were referred to me were excellent fits for our open positions. I appreciate the personal and professional expertise you offer to the process.
It is hard to put into words what nexus IT group has done for our organization. nexus IT group knows exactly what we look for and they get the right candidates 99% of the time. Anyone looking to hire tech talent would be lucky to have nexus IT group on their side. They have great work ethic, responsive, and a wide array of market knowledge.
We have been using nexus IT group for a few years now and they have done an outstanding job for us. nexus IT group has become an integral part of our success in filling several Cloud Engineer roles, and our partnership continues to grow. I would highly recommend nexus IT group to any hiring manager looking to staff their business with quality AWS candidates.
We’ve enlisted the help of a number of IT recruiting firms in the past. Nexus IT Group has contributed to the success of many great hires by working professionally, diligently, and efficiently. We contacted Nexus IT Group after being introduced to them, and we were instantly impressed by their promptness and ability to focus on our needs. We received 3 resumes the same day and 3 more the next day, and we were able to hire four people in less than a week. We added an additional individual through Nexus IT Group a few weeks later as a result of vast network, service and professionalism. I highly recommend working with Nexus IT Group.
Our machine learning staffing skills keep our clients coming back
Senior Machine Learning Engineer
A consumer fintech company building credit decisioning models for underbanked customers approached our machine learning recruiters when their internal team had spent four months trying to fill a Senior ML Engineer role focused on credit risk and fraud. The hiring bar was steep since they needed someone who had built and deployed gradient-boosted models in production at scale and who also understood model governance for regulated lending. Our team sourced 287 candidates from banks, lending startups and payments companies, narrowed the slate to a top seven then submitted four for client review. The client interviewed three of our four submissions and extended an offer to an engineer coming from a buy-now-pay-later firm. The hire was made within 23 days of kickoff.
MLOps Engineer
A vertical SaaS company in the legal tech space was scaling its AI features and needed an MLOps Engineer who could stand up a proper model training and deployment pipeline using Kubernetes, MLflow and Vertex AI. They had been receiving resumes from data scientists trying to pivot, none of whom had the infrastructure depth required. Our recruiters tapped into the platform engineering community on GitHub plus several specialized MLOps Slack groups, sourcing 198 candidates over the course of a week. We submitted six engineers with strong production experience. The client interviewed five and made two offers since headcount expanded mid-search. Both candidates accepted.
Head of Machine Learning
A late-stage healthtech startup tasked our recruiters with finding a Head of Machine Learning who could lead a team of fifteen across applied research, ML engineering and data science. The role required someone who had scaled an ML organization at a similar-stage company before, with bonus points for experience in regulated industries. We ran a confidential executive search since the existing VP of Engineering was being repositioned. Our team mapped out 142 potential leaders, had introductory conversations with 38 of them then presented a final shortlist of five. The client conducted full interview loops with three finalists and extended an offer to a candidate previously leading ML at a digital health unicorn. Diversity sourcing was a stated priority and three of five finalists came from underrepresented groups.
Computer Vision Engineer
A robotics company building warehouse automation systems needed three Computer Vision Engineers with deep experience in 3D perception, point cloud processing and real-time inference on edge devices. The role was onsite in a secondary tech market, which narrowed the candidate pool considerably. Our machine learning recruiters built a custom outreach campaign that highlighted the unique nature of the work, the autonomy of the role and the company’s recent Series C funding. We contacted 412 candidates nationwide, held screening conversations with 31, submitted nine and the client hired three within seven weeks. Two of the three relocated for the role.
Nexus IT Group recruits nationwide including these cities
Frequently asked questions about machine learning recruiting
To nexus IT group placement is an art, one that requires the proper alignment of talent with opportunity, experience with need, personality with environment, and aspirations with value. The machine learning hiring space is fast-paced, highly competitive recruiting environment that offers big opportunities for ML candidates. Our full-cycle recruiting system is a 51 step touchpoint process from start to finish. Our engagement process starts with a discovery call to understand the needs of the role. With this series of questions our machine learning recruiters are able to build a sales cadence that we will use to deploy a multi-touch recruiting cadence precisely and accurately to hundreds of candidates that fit the expectations of the role. Once our targeted search is executed, we spend a series of 3 calls to get to know the candidates’ needs, wants, and future career interests. Once we’ve created a shortlist of qualified and fully vetted candidates, we will deliver our top 3 to 4 candidates to the machine learning hiring manager. Once the interview process starts we work with the candidate and the hiring team to make sure communication is responsive and transparent. Once a candidate is placed, we stay in touch with the placed candidate for 2 years just to see how things are going.
Our candidate sourcing can be described in 3 words: Identify. Engage. Toolstack. Typically, the best job prospects are those who are passive. This indicates that they are not actively seeking employment. As a result, they are not surfing the Internet in search of online job postings. And because they are not viewing online job postings, we have developed a robust and 100% outbound recruiting strategy. We begin our sourcing strategy by creating a candidate persona. Once we have successfully created the candidate persona, we will then create a content strategy that will attract top talent in the machine learning space. Once we’ve developed our cadence we will use a number of sources to scour the web to get our message in front of the right candidates. Strategies include searching our database, sourcing for referrals, sourcing from specific organizations, LinkedIn, GitHub, Kaggle, Hugging Face, Papers With Code, NeurIPS and ICML alumni networks, Meetup, Angel List, Dice plus many other specialized machine learning sites. We craft highly personalized messages that drive above industry reply rates. Our recruiting strategy is to begin conversations with passive candidates to learn about their future career interests, needs and wants then align those needs with future openings with a high-touch approach.
Currently our 1 year retention rate with our machine learning clients is running right at 96.45%.
The machine learning hiring environment moves fast so our team of ML recruiters are trained to work quickly and accurately. The time required to send qualified candidates to the machine learning hiring manager varies based on the position you are seeking to fill and the challenges we will face such as lower than market compensation, bonus/equity concerns, slow feedback, onsite expectations and other variables. We can deliver applicants on the day of contract signing, within a few days, or within a few weeks.
We’ve spent a great deal of time recruiting in the ML space and have had the distinct pleasure of collaborating with a number of amazing companies pushing the boundaries of what software can do. From self-driving cars to drug discovery to generative AI tools that millions of people use every day, machine learning is reshaping nearly every industry. From our perspective, helping put the right people in front of these problems is genuinely meaningful work.
Machine learning recruiters specialize in a specific market sector. Every day, our ML recruiters contact a specialized set of machine learning candidates working with deep learning, computer vision, NLP, MLOps, generative AI and all things ML. When starting a new search, our team has a pool of personally-known candidates to call immediately. The point is that specialized machine learning recruiters sprint out of the gate more effectively than staffing firms that go back to the drawing board for each search.
When employers receive a resume from a machine learning recruiting firm, there is a high probability that they are familiar with the individual. Our clients trust that we have thoroughly screened the candidate over a series of multiple conversations and that they will be worth the hiring manager’s time to interview.
Another benefit of using a specialized machine learning recruiting firm is you will experience less of a “resume broker” relationship and more of a “sector expert.” We spend countless hours every day conversing with ML prospects and machine learning hiring managers, making us more “in the know” about the machine learning space. ML recruiters can provide insight into the landscape of employment, equity, compensation as well as the most recent candidate hiring and retention challenges.
Ultimately, machine learning recruiters have access to a concentrated pool of candidates, resulting in increased efficiency during the hiring process and the ability to establish long-lasting relationships with the industry’s top talent.
Whether you’re recruiting for a Sr Machine Learning Engineer, MLOps Engineer, Applied Scientist or Computer Vision Engineer, our recruiting solutions have you covered. Our hires range from just about anything you can imagine that a company would need when hiring machine learning talent, including non-tech roles like Head of Machine Learning, VP of ML, Director of Machine Learning and many more, with a niche focus in machine learning staffing.
Our machine learning recruiters use a series of stages to make sure we minimize candidate fall out during the recruiting cycle. Our recruiters will perform an in depth conversation where they work to uncover the needs, wants and desires of the candidate’s next career move. We ask candidates specific skillset questions covering modeling approaches, deployment experience, infrastructure knowledge and applied research background. If the team feels that there’s a fit then another person from our team will speak with the candidate. If the 2 nexus reps feel that there’s a good fit then we ask the candidate to complete a 4 question feedback form so we can better understand if the role is a good or great fit. Once we receive the candidate’s feedback we will perform a “Lockdown” conversation where we ask a series of questions around counteroffers, interview stages, target salary expectations and much more. If this goes well then we will submit the candidate for client review.
First, it is incredibly difficult for machine learning hiring managers with little time to receive responses to job ads. Second, an increasing number of professionals do not respond to recruiters, particularly in the ML field. They prefer to hear directly from the machine learning manager, which brings us to the third challenge: recruitment is a full-time, highly specialized profession for which machine learning hiring managers do not have time.
Our machine learning recruiting fee is 22% to 25% of the candidate’s first year base salary. We do our best to find a payment structure that works best for your team.
Our machine learning recruiters are paid by the companies for which they place candidates. Your machine learning recruiter does earn a commission if you are hired, but the commission is paid by the company that hires you. Our recruiting fee isn’t taken out of your salary and it doesn’t lower the salary that you would be offered. Another misconception is that if you are presented by a recruiter, and another candidate is not, that you would be at a disadvantage because you have a fee associated with you getting that role. In our experience companies are seeking the best person for the job and they are not concerned about a fee being paid at that point. In fact, candidates that work with recruiters actually tend to make more than those who job hunt alone.