AI Ops Research Engineer-Mid -Level
$93,770- $168,790
Apply (opens in a new tab)AI Ops Research Engineer-Mid -Level
Posted yesterday
SENIORITY
Lead
SALARY
$93,770- $168,790
About the role
- Research emerging AI, AIOps, observability, automation, and industry trends to identify technologies and approaches with potential value for Application and Infrastructure Operations.
- Translate operational challenges and emerging technologies into research hypotheses, experiments, prototypes, and measurable evaluation criteria.
- Evaluate machine learning, generative AI, agentic AI, predictive analytics, anomaly detection, root-cause analysis, and intelligent automation for operational use cases.
- Develop rigorous evaluation and testing methods to assess accuracy, reliability, scalability, failure modes, production readiness, and business value.
- Translate validated research into technical recommendations and production opportunities, identifying which capabilities should advance, be refined, or be discontinued.
- Communicate research findings and recommendations to engineering, product, risk, and leadership stakeholders, including key opportunities, limitations, risks, and investment considerations. What You Have:
- Bachelor's degree in a STEM or Business field with applied quantitative or behavioral research fields; OR 4 years of relevant education and/or experience.4+ years of experience in applied research, AI/ML, data science, software engineering, operations research, or related technical fields.
- Strong programming and analytical skills using Python and relevant AI/ML or data-analysis tools.
- Experience designing experiments, developing prototypes, analyzing results, and making evidence-based recommendations.
- Working knowledge of machine learning, statistical analysis, generative AI, and LLM concepts.
- Ability to rapidly evaluate emerging technologies and distinguish technical potential from production readiness.
- Strong written and verbal communication skills, including communicating complex findings to technical and nontechnical audiences.
- Knowledge of AI governance, model risk, data management, privacy, security, or operational risk principles. What Sets You Apart: Experience applying AI/ML to AIOps, observability, reliability engineering, infrastructure operations, or incident management.
- Experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn.
- Experience researching or prototyping generative AI, agentic AI, or autonomous operations capabilities.
- Experience building AI evaluation frameworks, including model comparisons, benchmarks, regression testing, and failure-mode analysis.
- Familiarity with AIOps and observability technologies such as Dynatrace, BigPanda, Moogsoft, Open
- Telemetry, Prometheus, or Grafana.
- Experience evaluating emerging AI technology in a regulated or highly controlled environment.
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
It's what we read first. Occupations, seniority and locations matter more than a long history.
Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.
