Hire a Dedicated Scala Developer
Hire a dedicated Scala developer and you get one engineer, full time, on your codebase, not someone splitting hours across three other clients this month. They sit in your standups, work your roadmap, and answer to it the way a full time hire would. Scala is the only thing we do, so the engineer you get has already shipped Cats Effect, ZIO, Spark, and Akka in production before they touch your repo.
// one engineer, fully embedded case class Engineer( seniority: Senior, stack: List["Cats Effect", "ZIO", "Spark", "Akka / Pekko"], allocation: Dedicated // not time-sliced )
Why dedicated
A dedicated Scala engineer becomes part of the system, not just extra hands on it.
A dedicated Scala developer works inside your codebase full time, not across a handful of accounts split between other clients. That means they carry context forward: your build, your streaming topology, your compliance constraints, your on-call rotation, instead of relearning it with every new ticket. For a team running Scala in production, where the cost of a wrong abstraction compounds for years, that context is the deliverable. Hire dedicated.
Where our engineers work
Systems That Benefit From a Dedicated Scala Engineer
Money code is where Scala's type system stops being a preference and starts being a control: ADTs and refined types make an unsettled transaction or an unhandled currency case a compile error instead of a 2am reconciliation. An engineer who has modelled ledgers in Scala before writes that domain differently than one learning it on your production traffic.
Spark is a Scala-native API, and the gap between a job that runs and a job that runs cheaply is entirely in the details: partitioning, shuffle boundaries, serialization, when to drop out of the DataFrame API and back into typed Datasets. That is tuning work measured in months of familiarity with your data, not a fixed-scope engagement.
Low-latency order flow and event-driven systems live or die on concurrency design: Akka and Pekko actors for sharded state, FS2 for backpressured streams, and a hard discipline about where blocking is allowed. Getting supervision, at-least-once semantics, and replay right is ecosystem knowledge, the kind that takes a specialist, not a JVM generalist.
The models are Python, the feature pipelines, online stores, and inference layers that keep them fed usually are not. Scala on the JVM is where teams put the throughput-sensitive half, and keeping training and serving features consistent is a long-running correctness problem, not a one-off build.
Most teams hiring us already have Scala engineers and a hiring pipeline that takes months to produce one more. A dedicated engineer joins the rotation you already run, your review standards, your Cats Effect or ZIO house style, your migration off Scala 2, and stays long enough to be the person who remembers why.
Scala is all we do.
Let's put that to work for you.
Tell us what you're building. We'll match you with an engineer who's already worked in your stack