Algorithm books on a range of topics
I have not (nearly) read enough books to name $5000 worth of them. Therefore, I will suggest some groups of literature you should cover as well as point you towards selected representatives. I can not claim to have read most of the books in full myself, so I have to rely mostly on descriptions, cursory impression and reputation. I have looked into or worked with most of them to some extent, or had them recommended by experts.

I assume that you want your people to learn what can be done, and how to do it, as opposed to learning what they can't do. In particular, I will leave out books about computability and complexity theory as such; I expect your people to have taken away the relevant messages from their undergraduate education. 


The Basics
Even though your people have learned them at some point, expect them to look
up the basics. Since sources like Wikipedia are frequently substandard 
or outright wrong, you want to get them proper reference texts.

Popular choices include


Introduction to Algorithms by Cormen, Leiserson et al.
Very broad introduction that covers many elementary algorithms and data structures as well as basic analysis techniques. A standard text used frequently for teaching purposes and available in its 3rd edition (so most mistakes should have been purged by now). Lots of exercises.
Algorithms by Sedgewick and Wayne
Another standard text in its fourth edition. Less broad than Cormen, but with more attention to implementation details. Lots of material online, including a free course on Coursera. Lots of exercises.
Introduction to Algorithms -- A Creative Approach by Udi Manber
Also rather slim a selection of topics, but presented with attention to didactics. The focus is on inductive strategies. Contains lots of exercises and solutions (or at least hints) for some. A good secondary reference if you don't like the recommended textbook because of its unusual style.
Concrete Mathematics by Graham, Knuth and Patashnik
Covers discrete mathematics as relevant to algorithm analysis. Rare in its focus on computer science needs and rigor. Very high quality. Lots of exercises with solutions.

Analysis of Algorithms
In most standard text books, analysis of algorithm usually stops at the level
of $O$-asymptotics and RAM model. In order to estimate real-world efficiency
more reliably, it can be necessary to investigate runtime more precisely and
consider effects like memory hierarchy and communication.


An Introduction to the Analysis of Algorithms by Sedgewick and Flajolet
Basic techniques for precise analysis of algorithms. Written by the 
pioneers of the field. Also has a free online course.
The Art of Computer Programming by Knuth
The reference for high-quality, low-level, precisely analysed 
algorithms for a whole range of problems. Also serves as reference.

Advanced Surveys


Purely functional data structures by Okasaki
Classic and basic literature often focuses on the procedural paradigm. If 
you want to work in the functional paradigm, new techniques for efficient
data structures are needed. This book is a detailed overview over the area.
Advanced Data Structures by Brass
Sometimes, the basic techniques are not enough. This is an overview over
advanced data structures, with code and many references.
Algorithmics for Hard Problems by Hromkovič
Complexity theory tells us (as practitioners) not to bother looking for
exact yet efficient algorithms for many natural problems. There are plenty
of techniques for practically solving these problems; this text shows
you how.

Specialised Literature


Compilers: Principles, Techniques, and Tools aka The Dragon Book by Aho et al
Should you ever need to build or tinker with a compiler, this is the
standard text on the area.
Network Flows: Theory, Algorithms, and Applications by Ahuja
Many problems can be modeled as network  flow problems; this book gives you
a full coverage of the field.
Probabilistic Graphical Models by Koller and Friedman
Graphical models are a major tool in modeling scenarios for machine
learning (among others) probabilistically. This is a comprehensive overview
about the complex. There is a related free online course.
Handbook of Exact String Matching Algorithms by Charras and Lecrog
String matching is an ever important task when dealing with data.
This book lists most (if not all) relevant algorithms for the job,
including high-level descriptions as well as implementations.
Analytic Combinatorics by Sedgewick
and Flajolet
The deep mathematical dive after "An Introduction to the Analysis of 
Algorithms" by the same authors. Not for everybody, but gold for the
interested.
Algorithms on Strings, Trees and Sequences by Gusfield
Should you ever have to deal with huge amounts of string data (in 
particular in biology contexts) this is the go-to book as it provides an
overview over the relevant data structures and algorithms.

For the Practitioner


Patterns for Parallel Programming by Mattson et al.
In practice, you might want to utilise multiple machines to deal with
large problems. This book is a high-level, example-driven overview 
over ways to do so, that is how to organise and move data and computing 
agents. It also addresses ways to implement them with existing tools.
A Guide to Experimental Algorithmics by McGeoch
When theoretical analysis is too hard or too coarse, you have to perform
experiments. This is an introduction into how to properly design 
experiments on algorithms.
The Definitive ANTLR 4 Reference by Parr
You often need domain specific languages and tools to parse them. ANTLR is
a mature and convenient compiler generator, and this is the book best
suited for learning how to use it. Parr also has some other books on
DSLs worth checking out.



If you want very recent material, you should consider getting your people access to journals and conference proceedings, maybe by cooperating with a university library. You can also actually let them attend conferences on topics relevant to them resp. your company.

Also, consider asking your people. Have them do their own research (including free samples or copies on the web or libraries) and they will tell you which books they consider relevant to their work. There is no use in buying stuff no one will read.