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ML Engineering
We search permanently for the engineers who run models in production and for the engineering leaders, product owners and operational staff accountable for them. Briefs are written around what the role actually owns: training, serving, platform or the teams behind them.
What this covers
- · Training pipelines and distributed training
- · Inference, serving, latency and cost optimisation
- · ML platform, feature stores, orchestration and MLOps
- · Applied ML in product teams, and engineering leadership over them
Roles we search for
- · ML Engineer / Applied Scientist
- · Inference or Serving Engineer
- · ML Platform Engineer
- · MLOps / ML Infrastructure Engineer
- · Engineering Manager, ML Platform
- · Head of ML or Director of ML Engineering
What the brief covers
- · Whether the role builds the platform or uses it
- · Current stack, and how much of it the hire is expected to change
- · Traffic, latency and cost targets the role owns
- · On-call reality and incident expectations
- · How success is measured in the first two quarters
What we ask candidates for, according to the role
- · Engineering: a model they took from notebook to production, and the parts that were hardest
- · Engineering: latency and cost figures they can describe, before and after their work, and how they handle regression in production
- · Platform: where they drew the line between platform work and product work
- · Product: a decision they made about what a model-backed feature would and would not do
- · Operations: a process they owned for releases, incidents or model monitoring
- · Leadership: how they staff platform and applied work, and how they measure the first two quarters
Things worth knowing before you start
- · Confirm whether the brief is platform, applied ML or both, and which is the priority
- · Confirm the traffic, latency and cost targets the role owns
- · Agree the assessment format up front, including whether any exercise is required and why
Questions we are asked
- Do you cover data engineering too?
- Yes, including data and analytics roles that sit alongside the ML platform in these markets. Send the brief and we will say whether it is a good fit for us.
- Can you help write the brief?
- Yes. A brief review is available before any search begins, and it is a separate conversation from terms.
- How do you present candidates?
- As a written introduction with role-relevant evidence, motivation, compensation expectation and notice, shared only after the person has given permission for that company.
Hiring in ML Engineering? Tell us what the role needs and we will say honestly whether we can help.
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