Junior AI Research Engineer Resume Example
Professional Junior AI Research Engineer resume example. Get hired faster with our ATS-optimized template.
Choose Your Level
Select experience level to see tailored resume template
Professional Junior AI Research Engineer resume example. Get hired faster with our ATS-optimized template.
View Template →Professional Middle AI Research Engineer resume example. Get hired faster with our ATS-optimized template.
View Template →Professional Senior AI Research Engineer resume example. Get hired faster with our ATS-optimized template.
View Template →Professional Lead AI Research Engineer resume example. Get hired faster with our ATS-optimized template.
View Template →Why This Resume Works
Verbs that signal research-to-prod ownership
Reproduced, Authored, Profiled, Extended, Implemented. Frontier labs scan for verbs that prove you can take a paper and turn it into runnable training code, not just 'used PyTorch'. This is the bar that separates research engineers from generic MLEs.
Eval and training-run numbers, not vibes
Within 0.6 points of HumanEval pass@1, 38 ablation runs, 17% of GPU-hours, 1.7x throughput. Research engineers are judged on benchmarked deltas; without the number, your ablation is folklore.
Rigor and FLOPs discipline visible in every bullet
Not 'trained a model' but 'across 3 distilled model sizes' and 'the 4 settings that survived golden-trace eval replay'. Frontier labs hire for rigor: ablations that prove a hypothesis, not training runs that burn compute. This is the part MLE-flavored CVs always miss.
Collaboration signal, even at intern level
In pair with two senior research engineers; landed in 3 internal training stacks. Even as an intern, prove you ship into shared codebases that other researchers depend on. This is NOT an MLE role; it is a paper-to-codebase role with peer reviewers.
Stack named at the layer a frontier lab cares about
Triton kernel, FSDP-Z2 sharding, golden-trace replay, EleutherAI lm-evaluation-harness. Do not write 'PyTorch'; write the specific layer of the training stack you touched. That is how research-engineer recruiters tell hobbyists from contributors.
Switch between levels for specific recommendations
Key Skills
- Python
- PyTorch
- JAX
- Hugging Face Transformers
- Slurm
- FSDP
- Weights and Biases
- lm-evaluation-harness
- Triton
- CUDA
- DeepSpeed-Z2
- Hydra
- MMLU
- GPQA-Diamond
- HumanEval
- MATH-500
- vLLM
- FSDP-Z3
- DeepSpeed ZeRO
- Megatron-LM
- NCCL profiling
- SFT
- DPO
- RLHF
- RLAIF
- PPO
- Hugging Face TRL
- DeepSpeed-MII
- Triton kernels
- NCCL
- Rust
- Tensor Parallel
- Activation Checkpointing
- Speculative Decoding
- Reward Modeling
- Constitutional AI
- Golden-trace Replay
- Scaling Laws
- Inference-Time Compute
- Mech-Interp Probes
- Mixture-of-Experts
- RLHF/DPO/RLAIF
- Multimodal Alignment
- Mech-Interp
- Red-Team Eval
- Eval-Harness Contracts
- FLOPs Accounting
- Org Design
- Research Strategy
- Hiring Rubrics
- Compute Budget Planning
Level Up Your Resume
Salary Ranges (US)
Career Progression
AI Research Engineering is one of the highest-leverage tracks in frontier labs. Progression goes from ablation-owner / eval-harness contributor (junior) to small-model training-run lead (middle) to large-model training-run-tier lead (senior) to research-area architect (lead, MTS, staff). Each level adds compute scale, eval-suite ownership, and reusable artifacts. The ceiling for ICs is staff or principal research engineer; many leads also pivot to research-engineering management (head of pretraining, head of post-training).
Reproduce 2-3 frontier-lab papers with named eval deltas, contribute one merged PR to lm-evaluation-harness / trl / vLLM, own a small-model ablation series end-to-end, profile and report GPU-hour cost, ship one Triton kernel or NCCL-tuning fix, and start being the named on-call for at least one secondary training run.
- FSDP-Z3 + activation checkpointing
- SFT and DPO post-training
- Triton kernel authoring
- Eval-harness golden-trace replay
- FLOPs accounting
Be primary on-call for a real training run (>=7B parameters) with a reliability percentage, kill at least one multi-week ablation with named eval evidence, mentor 2 juniors through their first ablation-owner rotations, author a reusable artifact (post-training run-book, eval template, kernel pack), and start influencing the eval-harness contract used by adjacent teams.
- RLHF and RLAIF post-training
- NCCL collective tuning
- Tensor parallel + pipeline parallel
- Speculative decoding stacks
- Reusable run-books
Own a frontier-tier training run (4-digit GPU count, 70B+ parameters, multi-week duration), produce a senior-only kill (multi-week initiative stopped after eval ablation, hundreds of thousands of GPU-hours redirected), mentor 2 ICs to research-engineer senior, author a company-wide eval-harness contract or FLOPs accounting library, and partner with a VP-level peer on the research-area roadmap.
- Research-area architecture (post-training, inference-time compute, multimodal alignment)
- Multi-million GPU-hour budget ownership
- Eval-harness contract design
- Promotion ladder design and IC rotation mechanisms
- Cross-team partnerships with VP-level peers
Adjacent paths: research scientist (more publications, less code), MLE / production AI engineer (serving and infra at scale), mech-interp researcher (specialized branch of the field), research-engineering manager (people leadership), inference-systems engineer (vLLM / TensorRT / speculative decoding specialist). Some research engineers also pivot to AI safety / red-team-specific roles or to founding research-tooling startups (eval platforms, training-stack tooling).
AI Research Engineer CV templates and examples from intern to lead, written for the actual frontier-lab job spec. The role lives between the research scientist and the production MLE: you turn papers into runnable training and inference code, own the eval harness, run ablations, and ship frontier-model components. Recruiters at Anthropic, OpenAI, Google DeepMind, FAIR, NVIDIA Research, Cohere, and Apple AIML scan for very specific signals: paper-to-checkpoint turnaround, training-run reliability percentages, eval-suite pass rates on MMLU, GPQA-Diamond, HumanEval and MATH-500, FLOPs efficiency, GPU-hour cost discipline, and the discipline to kill ablations that fail to lift evals. This guide covers junior to lead with concrete metrics, the tools that matter (PyTorch, JAX, FSDP, DeepSpeed ZeRO, Megatron-LM, Triton, RLHF, DPO, golden-trace replay), and the wording that separates research engineers from generic ML engineers.