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Engineering & Technology - Engineering

Software Engineer - Applied Inference

IC / Practitioner

Independently audited

Builds and operates the software the business runs on. Owns a defined area of the function and the outcomes it produces.

Audited package

Built, independently audited role package. Everything on this page is written for this seat.

Your Stack (Swappable)

This role works against whatever tools you already run. We list the tool categories the role operates in, never a named vendor, so you are never asked to replace a system that works.

AI / LLM Platform
Model runtimes and formats
Kernel and compiler tooling
Accelerator and cluster substrate
Gateway and admission
Observability & Monitoring

Deliverables

Design documents
Review checklists
Technical standards
Delivery and sequencing plans

Responsibilities

Characterize production model-serving workloads and define latency, throughput, quality, availability, fairness, capacity, and unit-cost targets.
Design and implement request admission, queueing, prioritization, cancellation, preprocessing, batching, cache, prefill, decode, postprocessing, and response streaming.
Optimize model execution through precision, quantization, compilation, kernels, graph capture, parallelism, memory layout, prefix reuse, and speculative methods where justified.
Engineer accelerator placement, model loading, warm pools, autoscaling, failover, multi-tenancy, quotas, and workload isolation.
Build performance and quality harnesses that reproduce prompt and media distributions, concurrency, bursts, long context, output length, and failure conditions.
Plan model and runtime rollouts through shadow, canary, side-by-side quality comparison, stop conditions, rollback, cleanup, and artifact traceability.
Diagnose queue growth, OOM, cache thrash, kernel regressions, low utilization, cold start, fairness, quality drift, and runtime or processor mismatch.
Partner with model builders, MLOps, Platform, SRE, Product, Safety, Security, Privacy, Finance, and Capacity owners without taking their mandates.

The essence of this seat

Signature move

Characterize the actual workload distribution before selecting a runtime: input length, media shape, output length, concurrency, burst, priority, cancellation, tenant, and quality envelope

What good looks like

uses correlated production distributions, stage decomposition, tail SLOs, goodput, and unit economics instead of a fixed microbenchmark

Decision rights

- the technical design and implementation choices inside the bounded request admission, prefill and decode, continuous batching, KV-cache management, quantization, compilation, kernels, accelerator placement, memory, model parallelism, autoscaling, multi-tenancy, TTFT, inter-token latency, throughput, tail latency, cost, rollout, fallback, and serving incidents work assigned to this seat
- the evidence, tests, safeguards, and operational artifacts needed to demonstrate this outcome: approved model artifacts meet an explicit production quality envelope while serving real workload distributions within latency, throughput, availability, memory, and unit-cost objectives
- when a change inside the delegated scope is ready for review, canary, rollback, or further investigation

What this role does

The Software Engineer - Applied Inference is individual contributor who is the engineer who makes trained models fast, efficient, reliable, and operable in production by owning the inference-serving path and its runtime behavior. The role is accountable for the engineering work required so approved model artifacts meet an explicit production quality envelope while serving real workload distributions within latency, throughput, availability, memory, and unit-cost objectives. Its working domain includes request admission, prefill and decode, continuous batching, KV-cache management, quantization, compilation, kernels, accelerator placement, memory, model parallelism, autoscaling, multi-tenancy, TTFT, inter-token latency, throughput, tail latency, cost, rollout, fallback, and serving incidents. A strong practitioner turns that domain into explicit contracts, measurable evidence, controlled changes, and clean hand-offs. The role reasons from the actual artifact and failure boundary rather than from a fashionable tool. It makes assumptions visible, tests the load-bearing ones, and distinguishes what is Verified, Inferred, and Not-yet-verified. The role remains at IC altitude: it owns technical execution and bounded decisions, advises leaders and neighboring specialists, and escalates product, people, budget, policy, legal, risk, and cross-organizational calls. The nearest boundaries are deliberately strict. Distinct from Multimodal AI Engineer: this seat optimizes and operates the inference runtime for the supplied model and processor contract; that seat builds, fine-tunes, and evaluates the multimodal model and its behavior. Distinct from Machine Learning Engineer: this seat owns the hot serving path and performance engineering of inference; that seat owns training systems, broader model lifecycle, feature and data pipelines, and general production ML integration. Distinct from MLOps Engineer: this seat owns deep runtime performance, scheduling, memory, kernel, and request behavior; that seat owns deployment workflow, registry, promotion, monitoring, and lifecycle across models. Distinct from AI/ML Engineer: this seat owns shared or specialized serving mechanics and performance; that seat owns the user-facing AI feature and its product outcome. Those titles still receive their own complete packages under the 1:1 policy.

Best-fit customer

engineering teams that want consistent judgement applied to design, review, and delivery

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