AI & Scheduling Automation Engineer, LSO Playout

Position summary

LSO Media Group is seeking an AI & Scheduling Automation Engineer to build practical assistance for the people who program and operate television and streaming channels. This full-time position reports to the Director of Product & Engineering, LSO Playout. Working on a forthcoming product, you will translate operator needs into features that can propose schedules, identify missing or inconsistent content information, surface conflicts and explain recommended actions. The work combines applied AI engineering, conventional business rules and a close understanding of programming workflows.

You will build a clear separation between recommendations and execution. AI-generated schedule changes must be inspectable, pass deterministic checks and receive human approval before they are published to the channel engine; language models will not directly control on-air timing. You will choose approaches based on measured task performance, cost and supportability, rather than assuming every workflow needs a model. AI development tools will also support your own work, with an expectation that you can read, debug, test and maintain the software they help produce.

Responsibilities

• Work with the director and broadcast operators to identify scheduling and content-review tasks where automation can save time with measurable, acceptable error rates.
• Build AI-assisted schedule proposals and metadata workflows using structured inputs and outputs that can be validated before they reach operator review.
• Implement conventional checks for timing conflicts, missing assets and configured programming constraints, keeping hard rules independent of model suggestions.
• Design review and approval flows that show proposed changes, supporting information and unresolved issues before an operator publishes a schedule version.
• Integrate approved schedule changes with the deterministic channel engine through bounded interfaces; keep model access separate from direct on-air timing and control.
• Develop content inspection and recommendation features that distinguish observations from uncertain inferences and route ambiguous results to a person.
• Create evaluation datasets and regression checks for realistic operator tasks, including incorrect suggestions, incomplete metadata and adversarial text within supplied content.
• Measure task accuracy, operator acceptance, latency and cost, and define fallback behavior when a model or external service is unavailable.
• Apply access controls, data minimization and traceable records to model requests and workflow actions in collaboration with the platform engineer and product director.
• Use AI engineering tools throughout development while documenting generated changes, verifying code and explaining system limits to product and support teams.

Required qualifications

• At least four years of software engineering experience, including delivery of an AI-assisted application, workflow automation system or comparable applied project.
• Strong programming skills and experience integrating model APIs or locally operated models with typed data, validation and conventional application logic.
• Experience creating task-specific evaluations, examining failure cases and using results to improve a system rather than relying on demonstrations alone.
• Ability to design approval workflows, permissions, audit records and failure handling for automation that affects consequential business operations.
• Demonstrated use of AI development tools with independent code review, debugging and testing; a specific AI vendor certification is not required.
• A degree in computer science, engineering or a related field, or equivalent practical experience, with the ability to learn broadcast workflows and communicate clearly with operators.

Preferred qualifications

• Experience with broadcast scheduling, content management, traffic systems or other planning workflows governed by explicit constraints.
• Experience with constraint-solving techniques, retrieval from approved data sources or evaluation of structured model outputs.
• Experience assessing model privacy controls, prompt-injection risks or human review practices in operational applications.

Measures of success

• Initial assistance features reduce effort on selected operator tasks, with quality measured against a documented baseline.
• Every proposed schedule change is reviewable, checked against defined rules and approved before it reaches the execution engine.
• Evaluation and monitoring expose unreliable outputs, regressions and unacceptable operating costs before wider use.
• Operators can understand recommendations, correct mistakes and continue essential scheduling work when AI assistance is unavailable.

First 90 days

During your first 90 days, you will map priority operator tasks, agree on approval boundaries and build an evaluation set from authorized sample content and schedules. You will prototype one focused scheduling or content-checking feature, compare it with a conventional baseline and demonstrate the complete proposal, validation and human-approval path. You will document failure cases, cost assumptions and integration needs so the director can decide which capabilities are ready for further development.

Compensation

Proposed annual base salary of $140,000–$185,000 USD. The final offer will reflect relevant experience, qualifications and the agreed scope of the position, subject to approval. This range covers base pay only; any incentive compensation or benefits would be specified separately in an employment offer.

What to include in your application

If available, include a nonconfidential AI or automation project that shows the original task, your implementation and how you evaluated results. Describe a failure you found, how you corrected it and where human review was required. Do not submit private prompts, customer data or proprietary source code.