New opportunity

Artificial Intelligence Researcher

Innovaccer Analytics · San Francisco, United States

About this role

Innovaccer Analytics seeks an Artificial Intelligence Researcher to build production-grade AI systems for complex problems at scale. Working with product, engineering, and data teams, the researcher will develop AI-powered applications, including LLM-based solutions, AI agents, retrieval-augmented generation (RAG), and intelligent automation workflows.

Responsibilities

Take ideas from research and experimentation through prototypes to production deployment and optimization. Choose model sizes, combine models into product systems, and meet product-level accuracy standards.
Write Python and PyTorch code, investigate training performance, design hypothesis-driven experiments and ablation studies, and build evaluations to test results critically.
Assess current research for practical use and explain findings to non-AI colleagues, including clinicians and operators whose feedback can identify incorrect outputs.

Requirements

An MS or PhD in Computer Science, Machine Learning, or a related quantitative field. Exceptional BS candidates with substantial research or open-source work will also be considered.
Evidence of depth beyond coursework, such as first-author publications at NeurIPS, ICML, ICLR, ACL, EMNLP, or similar venues; meaningful open-source ML contributions; an AI-lab research internship; or models trained, shipped, and used by others.
Hands-on experience fine-tuning an open-weight model and the ability to explain a choice between parameter-efficient and full fine-tuning. Strong Python and PyTorch skills; familiarity with training and serving tools such as Hugging Face, FSDP or DeepSpeed, and vLLM or SGLang, or equivalents.
Some exposure to multi-GPU training, including an understanding of sharding strategies, and evidence of completing projects.

Benefits include PTO accruing at 20 days per year, parental leave, monetary rewards and recognition, medical, dental, and vision coverage, company-sponsored short- and long-term disability and basic life insurance, and discounted legal aid and pet insurance options.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonPyTorchLarge Language ModelsAI agentsRetrieval-augmented generationExperiment designAblation studiesModel evaluationModel fine-tuningParameter-efficient fine-tuningHugging FaceFSDPDeepSpeedvLLMSGLangMulti-GPU trainingModel shardingResearch communication

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Carry research through deployment
The role spans reading research, prototyping, production deployment and optimization; the posting also asks for evidence of finishing work. Prepare a concrete account of how an idea was tested, built and brought to a usable endpoint.
Build a product-level model system
The posting calls for choosing model sizes, combining models into a working product and meeting a product-level accuracy bar, including for LLM, agent, RAG or automation applications. Prepare to explain system choices and how their combined output was judged.
Evaluate before optimizing
The role emphasizes building evaluations early, stating hypotheses, running ablations and reporting results that contradict expectations. Prepare to explain what an evaluation measured, what an ablation isolated and how the findings changed a decision.
Make training and serving choices deliberately
Strong Python and PyTorch, hands-on open-weight fine-tuning, training-performance diagnosis and some multi-GPU exposure are stated requirements. Prepare to explain a parameter-efficient versus full fine-tuning choice and why a sharding strategy mattered.
Translate research across teams
The researcher works with product, engineering and data teams and explains findings to non-AI colleagues, including clinicians and operators who may identify wrong outputs. Prepare to communicate both the practical value and limitations of a technique without assuming those audiences agree with model-only metrics.

Likely challenges to think through

Promising results versus reliable outputs
A likely challenge is that a strong experiment result may not satisfy the stated product-level accuracy bar or match errors noticed by clinicians and operators. Prepare to describe how you would define evaluations early, investigate disagreements and revise the system rather than rely on one favorable metric.
Model quality versus compute and serving cost
Choosing model size, fine-tuning approach and multi-GPU sharding may involve trade-offs between quality, training resources and deployability; the posting does not specify target budgets. Prepare to explain how you would compare options using measured performance and product requirements.
Research novelty versus production value
The role asks the researcher to assess current work while taking ideas through deployment. A plausible difficulty is deciding when a newer technique is worth integrating into a composed AI application. Prepare to explain how you would test its incremental value against a simpler baseline and communicate the result to partner teams.

Practice your answers

Technical practice

How would you define a product-level accuracy bar before building an AI model?

What to coverSuggested preparation: Describe how you would choose representative cases, establish a baseline, and define failure categories before development. Explain how you would investigate a result that looks unusually good rather than accepting it at face value.

How would you evaluate a retrieval-augmented generation application?

What to coverSuggested preparation: Distinguish retrieval failures from generation failures and explain how you would test each. Discuss when improving retrieval may be more useful than changing the language model.

When would you use an AI agent rather than a simpler automation workflow?

What to coverSuggested preparation: Compare the flexibility of an agent with the predictability of a defined workflow. Explain how you would test whether the added complexity improves the application.

How would you choose model sizes and combine models into a product system?

What to coverSuggested preparation: Describe the trade-offs among task accuracy, speed, and compute cost. Explain how you would evaluate the complete system against a product-level accuracy bar, not just each model in isolation.

How would you choose between parameter-efficient and full fine-tuning of an open-weight model?

What to coverSuggested preparation: Compare resource requirements, flexibility, and expected task-specific gains. Describe the baseline and evaluation you would use to determine whether fine-tuning helped.

A PyTorch training run is slower than expected. How would you investigate it?

What to coverSuggested preparation: Walk through measurements that distinguish data-loading, compute, and communication bottlenecks. Explain how you would verify that a proposed optimization improves throughput without invalidating the experiment.

Why does a sharding strategy matter in multi-GPU training?

What to coverSuggested preparation: Explain what can be distributed across devices and how memory savings can introduce communication costs. Discuss how you would choose and validate a strategy for a training run, using FSDP, DeepSpeed, or an equivalent only where relevant.

How would you design an ablation study for a promising AI-system change?

What to coverSuggested preparation: State a testable hypothesis, identify the component to remove or vary, and keep comparisons interpretable. Explain how you would report a result that contradicts your hypothesis.

What would you check when moving an LLM prototype into production serving?

What to coverSuggested preparation: Discuss how you would compare prototype and deployed behavior against the same evaluation criteria while measuring serving performance. Explain the trade-off between throughput or latency improvements and any change in output quality; vLLM, SGLang, or equivalents are relevant examples from the posting.

How would you decide whether a recent research technique is practical for this role?

What to coverSuggested preparation: Assess the evidence, reproducibility, implementation effort, and fit with a production-grade application. Describe a bounded experiment that could reveal whether the technique holds up beyond its reported results.

Behavioural practice

Tell me about a time you carried a research idea or prototype toward a finished, usable result.

What to coverSuggested preparation: Use situation, action, and result to show what you personally completed and where the work ended. Explain any trade-off between experimental performance and practical use without implying that every project reached production.

Describe a time you worked with product, engineering, or data colleagues to define an AI problem.

What to coverSuggested preparation: Set out the situation, your actions to resolve differing priorities or definitions of success, and the result. Be specific about how collaboration changed a technical decision.

Tell me about a time a domain expert identified an incorrect model output.

What to coverSuggested preparation: Describe the situation, how you investigated and responded to the feedback, and the result. If your example involves a clinician or operator, explain how their judgment informed evaluation without claiming the model became error-free.

Describe an experiment whose result contradicted your hypothesis.

What to coverSuggested preparation: Explain the original hypothesis and controls, the unexpected result, and the action you took afterward. Include what you communicated to others and what changed as a result.

Tell me about a time you explained AI research to people without an AI engineering background.

What to coverSuggested preparation: Give the situation, how you translated the finding and its limitations, and the result for the audience's decision. Distinguish evidence from uncertainty rather than presenting a technique as ready for use without support.

Sharpen your knowledge

Topics to prepare

Fine-tuning trade-offs
The posting requires experience fine-tuning an open-weight model and explaining parameter-efficient versus full fine-tuning. Suggested exercise: compare their trainable parameters, memory needs, expected benefits and limitations for one defined task, then justify a choice.
Multi-GPU sharding
The posting asks for some multi-GPU exposure and an understanding of why sharding matters. Suggested exercise: sketch how model parameters, gradients and optimizer state are distributed under a sharding strategy, and explain the memory and communication trade-offs.
Evaluation and experimental reasoning
The role emphasizes building evaluations before models, stating hypotheses and reporting ablations even when results disagree with expectations. Suggested exercise: define a product-level accuracy measure, a baseline and one ablation for an AI application, including how you would inspect incorrect outputs.
Model-layer system design
The posting names LLM applications, agents, RAG and automation, and asks for model-size choices and composition of models into a working product. Suggested exercise: diagram one such system, identify each model’s role and explain how you would check end-to-end accuracy.
Research-to-production decisions
The role spans reading research, prototyping, deployment and optimization. Suggested exercise: take one relevant research technique and outline evidence that would justify prototyping it, criteria for deploying it and reasons to reject it.

Skills to revise

Python and PyTorch implementation
Strong Python and PyTorch are explicit requirements. Suggested exercise: write a small training loop, verify its outputs and explain how you would locate a slow training step.
Open-weight model fine-tuning
Hands-on fine-tuning is explicitly required; the posting names Hugging Face or equivalents in the training stack. Suggested exercise: fine-tune an open-weight model on a small task, record the configuration and evaluation results, and explain the tuning approach chosen.
Training-performance diagnosis
The posting expects systems sense about slow runs and familiarity with FSDP or DeepSpeed or equivalents. Suggested exercise: inspect timing and memory measurements from a training run, identify a likely bottleneck and explain whether sharding would help.
Ablation and error analysis
Hypothesis-driven experiments, ablations and critical evaluation are stated responsibilities. Suggested exercise: remove one component from a prototype, compare it with a baseline on the same evaluation set and report both gains and regressions.
Application prototyping and serving
The role involves deploying AI applications and names vLLM or SGLang or equivalents as serving-stack familiarity. Suggested exercise: prototype an LLM-based application, test representative inputs through its served model and document where outputs fail. This is a preparation exercise, not a specified employer project.
Explaining results to non-AI colleagues
The posting calls for explaining work to clinicians and operators and using their feedback on wrong outputs. Suggested exercise: give a short, jargon-light account of an evaluation result, show two errors and ask what makes each output unacceptable.

Your preparation checklist

Assemble evidence of research depth
The posting asks for work beyond coursework. Prepare a concise record of any relevant publications, open-source contributions, research internships, or models trained and used by others, with your specific contribution and a verifiable outcome.
Rehearse a project walkthrough
The role spans research, prototyping, deployment, and optimization. Choose a completed project, if available, and prepare a walkthrough of the problem, decisions, Python/PyTorch implementation, results, collaborators, and what reached users.
Prepare an evaluation and ablation example
The posting emphasizes building evaluations early and reporting results that challenge a hypothesis. Bring an example showing the hypothesis, baseline, metric, ablation, unexpected result, and resulting decision.
Explain an open-weight fine-tuning decision
The role requires hands-on fine-tuning. Prepare an accurate account of a model you fine-tuned, including why you chose parameter-efficient or full fine-tuning, how you evaluated it, and what you would change. Do not present a hypothetical exercise as past work.
Review training and serving trade-offs
Prepare to explain a training bottleneck you investigated, what multi-GPU sharding changes, and how tools such as FSDP or DeepSpeed and vLLM or SGLang fit into training or serving. Distinguish hands-on experience from conceptual familiarity.
Sketch a product-level AI design
As a practice exercise, outline an LLM, RAG, or agent-based application: model-size choices, component boundaries, an accuracy evaluation, and a route from prototype to deployment. This is preparation, not a stated interview assignment.
Practice explaining a finding to non-AI colleagues
The role involves product, engineering, data, clinicians, and operators. Draft a short explanation of one model result, its limitations, and how feedback about incorrect outputs would inform the next experiment.
Write questions for the employer
Prepare questions about how product-level accuracy is defined, who reviews incorrect outputs, and how research prototypes are evaluated for production use. These follow from the advertised responsibilities; the posting does not specify the answers.

Availability last checked: Nov 4, 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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