by Jeremy Karnowski and Emmanuel Ameisen
Applied AI roles involve a combination of software engineering and machine learning and are arguably some of the most difficult roles to break into. These roles, focusing on advanced algorithms and leveraging new research results for sophisticated products, are a core component in many organizations, from large corporations to early-stage startups.
While at Insight, we worked with top AI teams in the Bay Area and New York to help Fellows make the transition to Applied AI. Fellows from our first AI session joined teams like Salesforce R&D, Microsoft AI Research, Google, Quora ML, and Arterys (who has the first FDA-approved deep learning medical intervention).
Because of our unique position in this space, we want to share with a wider audience some industry insights, perspectives on how companies are building their teams, and skills to prepare for the transition, whether you are coming from academia or industry.
The Industry Perspective
After speaking with 50+ Applied AI teams in industry, including those building new products using advanced NLP, architecting deep learning infrastructure, and developing autonomous vehicles, the one thing we consistently see is that there is a spectrum for Applied AI practitioners — ranging from research to production.
- Research: On one end of the spectrum, teams are developing new ideas, doing R&D, developing prototypes, and primarily looking to produce papers.
- Production-level ML: On the other end of the spectrum, teams are taking current ideas and producing fast, efficient, and robust systems that can be embedded in products.
While there are roles in R&D labs and in teams doing deep learning research, the majority of roles exist in the middle of this spectrum, on teams that aim to simultaneously stay current with research and embed the best advances into products. Often teams have a mix of members working to achieve this, but you’ll be the most competitive if you can position yourself to add value in both areas — be able to read and digest current research and then implement in production.
Building a pipeline from research to production requires companies to structure their teams in a way that blends the benefits of both worlds — academic research and software engineering. Accordingly, the Insight AI program was structured in a similar fashion, bringing together academics doing ML and deep learning research and software engineers with experience in ML.
While all the AI Fellows have strong coding abilities and experience with deep learning, academics and software engineers have different strengths, so our advice for how to succeed for these two groups is different. In addition to Insight’s resources on getting prepared for Data Science, we’ve gathered additional resources that target the transition to Applied AI.
These two guides are a distillation of many conversations we’ve had with top teams in the Bay Area and in New York, who hire AI practitioners poised to tackle their technical challenges and accelerate their expansion into Applied AI.
- Preparing for the Transition to Applied AI (for Academics)
- Preparing for the Transition to Applied AI (for Software Engineers)
(this post was originally hosted on Medium)