Jacksonville, FL
ML engineering recruiting for production model teams. Crosscheck recruits AI/ML & LLM Engineering candidates for contract, contract-to-hire, and permanent roles tied to Jacksonville.
What We Place
Representative roles
Platforms and technologies
Our Approach
This editorial hiring guide starts with sourced Jacksonville business context. Jacksonville's Office of Economic Development separates advanced manufacturing, aviation and aerospace, finance and insurance, information technology, life sciences, and logistics and distribution. Those sectors create distinct requirements for transactions, protected records, products, and freight. A Machine Learning Engineer search needs a defined prediction task, training data owner, deployment path, and measure of useful performance. The same title can describe notebook research, feature engineering, backend development, or ownership of an inference service.
Screen ML engineers on modeling fundamentals and production tradeoffs
Match experience to the ML stack, data, and problem defined in the brief
target a first candidate slate within 48 hours for qualified exclusive searches in our core disciplines after a completed intake
Permanent placements include a 90-day replacement guarantee, subject to the signed agreement.
Start the search
Include the business context, systems, delivery phase, work model, compensation, and interview timeline. A Crosscheck search lead will use that context to calibrate the role before sourcing begins.
Local Market Brief
Write the brief around the model lifecycle. Include label creation, feature pipelines, experiment tracking, service integration, monitoring, and retraining duties that belong to this hire. Separate those duties from work owned by data, platform, or research teams. The three sourced Jacksonville contexts below turn that scope into intake and screening decisions. They do not measure current vacancies, candidate supply, or Crosscheck client activity.
Editorial market scenario
Tie each must-have requirement to a task, system, risk, or deadline. Remove industry preferences that do not change how the person will perform the job. This is planning guidance, not measured local demand.
Editorial industry scenario
A finance-facing brief should identify the transaction, reporting, audit, privacy, and availability requirements attached to the role. Confirm that this context applies to the employer before using it in the search.
Screening focus
We test for model or application ownership, evaluation discipline, data judgment, and evidence that the candidate has shipped reliable AI systems.
Published labor benchmark
BLS does not publish an occupation matching ML Engineer. Crosscheck uses Data Scientists (15-2051) as the closest published broad benchmark; it is not a count or pay estimate for this exact specialty.
BLS OEWS May 2025, published May 15, 2026
Published metro employment
880
BLS publishes fewer than one thousand metro jobs for the proxy occupation. Treat the estimate as a reason to define location flexibility before outreach. The estimate equals 1.146 jobs per one thousand across the metro workforce.
Employment concentration
0.68 location quotient
Jacksonville, FL reports a below-national employment concentration for this proxy occupation. Decide which requirements justify a wider regional or remote search.
Annual wage reference
$62,710 to $165,370
The metro median sits within five percent of the national Data Scientists median. Validate the budget against seniority, scope, and current salary data. BLS reports a $122,680 median for the proxy occupation in Jacksonville, FL.
Hiring brief scenarios
These scenarios connect location context to role responsibilities. Use them as prompts to verify with the employer, not as measures of Jacksonville demand, clients, or candidate supply.
Sourced finance, insurance, and information technology context
Jacksonville's Office of Economic Development lists finance and insurance and information technology among its targeted industries and connects the technology cluster with the city's financial-services base. Tie the sector scenario to a concrete outcome and data-generating process. Ask how the engineer would detect label leakage, sampling bias, missing history, and a metric that looks strong but fails the business use case. These systems can join customer accounts, policies, payments, claims, identity, fraud controls, digital services, approvals, reconciliations, reporting, and release support.
Evidence to request: Review a feature or training pipeline and ask the candidate to trace one record from source through validation, training, and evaluation. Name the product and transaction, system of record, money or claim flow, identity model, control owner, reporting deadline, reconciliation, release path, and support target.
Sourced life sciences context
The Jacksonville economic-development page names life sciences as a targeted industry and describes medical research facilities, health systems, and medical products and services. Define the inference environment before screening. Batch scoring, low-latency APIs, edge deployment, and analyst-facing tools require different software design and operating evidence. Life-sciences work may cross research data, patient records, laboratories, medical products, protected information, validation, manufacturing, billing, and enterprise systems.
Evidence to request: Use a production scenario with throughput, latency, and reliability limits. Require an API or batch design plus a test plan. Set the research, product, care, or administrative boundary, source record, validation protocol, device or lab interface, access controls, release authority, and reviewer.
Sourced manufacturing, aerospace, and logistics context
Jacksonville also targets advanced manufacturing, aviation and aerospace, and logistics and distribution and describes city port, airport, rail, interstate, and industrial-site infrastructure. Assign responsibility for drift, retraining, and model retirement. Candidates should explain the thresholds, review cadence, and human decision that follows an alert. These operations can connect engineering definitions, plants, aircraft or components, quality, maintenance, inventory, ports, warehouses, carriers, status events, and financial settlement.
Evidence to request: Ask for a model degradation example and the evidence used to separate data change, code change, and user-behavior change. Trace the product, aircraft component, or shipment through source, production or maintenance, quality release, inventory, transport, customer handoff, exception, and accounting.
Interview scorecard
Ask each candidate the same core questions. Score the evidence, ownership, and judgment in the answer instead of relying on job-title or keyword matches.
1. ML Engineer: PyTorch
Choose a PyTorch decision from your work as ML Engineer. Which constraint changed the design, and what evidence supported the result?
Use the answer to assess model evaluation, sensitive-data handling, explainability, and controls around automated decisions. The finance and insurance systems context is an editorial scenario, not a measured claim about Jacksonville.
2. Senior ML Engineer: TensorFlow
Describe project work you completed as Senior ML Engineer involving TensorFlow that did not follow the original plan. What did you own, and how did you correct it?
Use the answer to assess forecast or routing evaluation, changing inputs, latency, exception handling, and business review. The logistics and distribution operations context is an editorial scenario, not a measured claim about Jacksonville.
3. Staff / Principal ML Engineer: JAX
For a JAX system you supported, explain the handoff, operating limits, and measures used after launch. Where did your responsibility begin and end?
Use the answer to assess model evaluation, sensitive-data handling, explainability, and controls around automated decisions. The finance and insurance systems context is an editorial scenario, not a measured claim about Jacksonville.
Need the full ML Engineer evaluation guide?
The role guide covers technical scope, interview questions, and evidence checks once, without repeating the same material on every city page.
FAQ
For qualified exclusive searches in our core disciplines, Crosscheck targets a first candidate slate within 48 hours after a completed intake. Contract placements include a 40-billable-hour replacement guarantee, and permanent placements include a 90-day replacement guarantee, subject to the signed agreement.
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