AI is changing how software gets built, but it hasn’t eliminated the need for experienced engineers who can make good architectural decisions, review AI-assisted work, and deliver secure, reliable systems.
The hiring signals are mixed. An analysis of Indeed-based postings found software-development job listings had risen nearly 15% from late February 2025 to May 2026, with senior roles accounting for much of the recovery. In a separate October 2026 snapshot, only 5.1% of software-engineer postings were explicitly open to entry-level candidates. That figure is a point-in-time sample, not a universal measure, but it illustrates how selective entry-level demand can be. (Posting analysis, Indeed-based postings index at FRED, October 2026 snapshot)
Meanwhile, Robert Half’s 2026 technology hiring research reports that 78% of technology leaders planned to increase full-time hiring and 66% planned to add contract professionals in the second half of the year. Those plans can overlap: many businesses need both permanent team growth and flexible skills for specific initiatives.
If your software engineering staffing strategy hasn’t adjusted to this market, here are seven common mistakes, and practical ways to reduce their cost.
1. Posting entry-level roles without a development plan
A junior opening can be a sensible investment when the role includes coaching, code review, and a path to meaningful ownership. But simply posting “junior full-stack developer” and hoping an early-career hire can independently accelerate a complex project is a different proposition.
The potential cost is a stalled requisition and delayed work. A junior engineer who lacks consistent mentorship may also take longer to become productive, placing extra demands on the senior team.
Try this instead: For urgent work, hire an experienced engineer and pair that person with junior staff. If you need to build an early-career pipeline, define the mentor, training time, and first 90-day outcomes before recruiting. For a project with uncertain long-term needs, consider a contract-to-hire structure, but be clear about the evaluation criteria from the outset.
2. Treating AI capability as a bonus rather than a job requirement
AI-adjacent skills are showing up across software roles, not just dedicated machine-learning positions. Engineers may need to integrate AI features into existing products, assess generated code, or help establish safe usage practices. Robert Half’s 2026 analysis reports that AI, machine learning, and data-science postings rose 163% year over year in 2025, while employers also prioritized skills such as AI adoption, governance, continuous integration, and DevSecOps.
If your job description says nothing about how AI fits into the work, candidates may not understand the role’s priorities. And if you require “AI experience” without defining what that means, you risk screening for buzzwords instead of practical ability.
Try this instead: Be specific. Ask for examples of production work: evaluating AI-generated code, integrating a model or service, setting guardrails, or measuring quality, security, latency, and cost. Match the requirement to the actual project: an engineer shipping an AI feature does not necessarily need the same background as an ML infrastructure specialist.
3. Defaulting to permanent hiring for every need
A permanent employee can be the right choice for ongoing product ownership and long-term capability. But not every initiative requires a permanent hire. A defined cloud migration, security remediation, or AI pilot may need specialist skills for a limited period.
The cost of choosing the wrong model can run in either direction: adding ongoing payroll for a short project, or repeatedly restarting a search for expertise your team needs now.
Try this instead: Start with the work. Is it an enduring responsibility or a time-bounded deliverable? Contract software engineers can add capacity for a project or close a skill gap while you assess longer-term needs. If you may want to convert a contractor, confirm the terms and expectations before the engagement begins.
This flexibility is consistent with employer plans: Robert Half found that 66% of technology leaders expected to increase contract hiring in the second half of 2026, alongside 78% planning to add full-time staff. For more on choosing an engagement model, explore AList’s contract staffing perspective.
4. Using compensation bands that no longer match the role
A salary range that worked for a generalist opening may not attract someone with senior-level architecture, cloud, security, or AI integration experience. Compensation varies by location, specialization, and total rewards, but candidates will notice when expectations and budget don’t line up.
Robert Half’s 2026 national salary guide lists a starting-salary range of $109,250 to $175,500 for software engineers, with a midpoint of $142,000. These are employee salary benchmarks, not contractor bill rates, and they are not a substitute for checking your local market. Still, they are a useful prompt to review whether your budget fits the scope and level you’re asking for.
Try this instead: Set the level and compensation range before posting. Compare the role with current regional benchmarks, then make explicit trade-offs among experience, location, scope, and flexibility. If the budget is fixed, prioritize the outcomes that matter most rather than advertising a senior-level wish list at a junior-level range.
5. Letting the interview process stretch for weeks
A lengthy hiring process has real costs: more time spent coordinating interviews, a greater chance of losing a qualified candidate, and a longer wait before the work moves forward. Hiring benchmarks vary, but SmartRecruiters’ 2025 technology benchmark reports a median time to hire of 48 days. That is a useful warning about how easily a search can extend, not a reason to assume every role should be filled in 15 days.
Try this instead: Set an internal goal to make an offer within 15 days of identifying a qualified finalist, where practical. This is an operating target, not a market-wide benchmark. Use a structured technical assessment, agree on decision criteria in advance, consolidate interview rounds, and reserve time for the hiring team to make a timely decision.
6. Writing specifications that filter for tools instead of results
A long checklist of frameworks can make a role look precise while eliminating people who could solve the problem. It can also distract candidates from the actual challenge: what they will build, improve, or support.
Try this instead: Describe the outcomes first. For example: “Improve service reliability and reduce deployment friction” is more informative than a long list of tools without context. Then separate essential skills from skills a new hire can learn. In interviews, evaluate relevant work samples and real problem-solving rather than trivia about a specific version of a tool.
You can also widen the talent pool without lowering the bar. Consider candidates with transferable experience, including professionals returning to the field or veterans with relevant technical and leadership skills. AList shares more on veteran recruitment in technology and engineering.
7. Trying to source specialized talent through one channel
Job boards can help attract active applicants, but they don’t always reach experienced engineers who aren’t actively applying. For specialized work (such as AI integration, platform engineering, or secure systems) relying on a single sourcing channel can narrow your options and stretch the search.
The business impact is not just the recruiter’s time. Robert Half reports that 71% of technology leaders experienced project delays because of skills shortages in the past year; 52% said software engineering and development initiatives were affected. That makes effective sourcing a delivery issue as much as a hiring issue.
Try this instead: Use a mix of referrals, targeted outreach, internal mobility, and specialist recruiting. A staffing partner can help clarify the role, surface relevant candidates, and coordinate the process, especially when you need high-level software engineer placement or specialized tech talent sourcing. AList’s IT staffing services are built around a collaborative partner process, not a transactional résumé handoff.
Make the hiring plan fit the work
The best staffing decision starts with a clear picture of the work: what needs to be delivered, which skills are essential, how long the need is expected to last, and what success will look like. That clarity helps you choose the right level, compensation, employment model, and interview process.
To assess the cost of a delay, estimate the business impact of the milestone at risk and the number of days it may slip. Keep that estimate separate from salary or recruiting costs so you don’t count the same impact twice. It’s not a precise forecast, but it can help leaders compare hiring options and prioritize urgent roles.
Whether you need contract software engineers for an AI initiative or permanent high-level software engineer placement, AList Professionals brings 30+ years of experience across its focus industries and a national reach. We work with clients to understand the outcomes they need, then connect them with talent through a collaborative, outcome-focused process. Talk with AList Professionals about your hiring needs.
