Gen AI Engineers (Claude code, Codex, Cursor)
- Salary
- $90K–$130K
- Hiring from
- United States
- Work type
- Remote
- Posted
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Accellor is an AI-native services firm focused on delivering measurable business outcomes through advanced AI, data, and engineering capabilities. We help global innovators and Fortune 100 enterprises modernize their architectures, scale operations, and operationalize intelligence across the enterprise.
Our core focus centers on transforming advanced AI from proofs-of-concept into production-grade systems. Spanning AI solutions, robust data services, enterprise applications, and product engineering — we tailor our offerings to the distinct operational needs of industries such as healthcare, life sciences, telecom, retail, financial services, and technology.
Operating as an extension of our clients' teams, we combine design thinking with technology-agnostic architectures to ensure faster time-to-value and seamless system interoperability. From initial strategy to full-scale deployment, we build intelligent, connected ecosystems that drive sustained enterprise value and redefine the future of transformation.
Role Summary:
We are looking for Gen AI Engineer with strong problem-solving ability, exceptional Claude code, codex expert, cursor, solid Python engineering skills, and hands-on understanding of machine learning, deep learning, GPUs, and CUDA fundamentals. The ideal candidate may be early in their career but must be technically sharp, curious, implementation-driven, and capable of learning complex AI systems quickly.
This role is suited for someone who can build models, debug training issues, optimize GPU workloads, understand tensor operations, and work closely with research, platform, and engineering teams.
Key Responsibilities:
- Design, build, train, and evaluate AI/ML models using Python, TensorFlow, PyTorch, or JAX.
- Solid skills with Claude code, codex, Cursor
- Can do large number of PRs per month
- Develop clean training pipelines with data loading, checkpointing, logging, validation, and experiment tracking.
- Work on deep learning models including CNNs, Transformers, LLMs, embeddings, and attention-based architectures.
- Solid builder with architect mindset
- Debug model issues such as poor convergence, overfitting, unstable loss, NaNs, tensor-shape errors, and GPU memory failures.
- Run and optimize models on GPUs with awareness of CUDA execution, memory usage, batching, mixed precision, and kernel performance.
- Profile training and inference workloads to identify bottlenecks in compute, memory, data loading, and communication.
- Support development of custom operators, fused kernels, CUDA/Triton-based optimizations, or framework-level performance improvements.
- Build AI inference pipelines with focus on latency, throughput, reliability, cost, and quality.
- Create evaluation pipelines for model accuracy, robustness, hallucination risk, safety, latency, and regression testing.
- Read research papers, implement ideas, run experiments, and clearly document findings, trade-offs, and limitations.
Requirements:
- Strong programming experience in Python.
- Good understanding of data structures, algorithms, numerical programming, and clean code practices.
- Hands-on experience with TensorFlow, PyTorch, or JAX.
- Strong fundamentals in:
o Deep learning
o Backpropagation
o Gradient descent
o Optimizers
o Loss functions
o Regularization
o CNNs
o Transformers
o Attention mechanisms
o Embeddings
o Model evaluation
- Experience training and debugging models on CUDA-enabled GPUs.
- Basic understanding of:
o GPU memory
o CUDA kernels
o Threads, blocks, and warps
o Memory bandwidth
o Kernel launch overhead
o Mixed precision training
o CUDA out-of-memory debugging
- Comfortable with Linux, Git, Docker, shell scripting, and experiment management.
- Ability to reason from first principles and solve ambiguous technical problems.
Preferred Skills:
- CUDA C/C++ or Triton kernel development.
- C++ exposure for performance-critical AI systems.
- Distributed training using PyTorch DDP, TensorFlow Distributed Strategy, JAX, NCCL, MPI, or similar tools.
- Experience with GPU profiling tools such as:
o NVIDIA Nsight Systems
o NVIDIA Nsight Compute
o PyTorch Profiler
o TensorFlow Profiler
- Experience with inference optimization, quantization, TensorRT, ONNX, XLA, or model compilation.
- Exposure to LLMs, RAG systems, agents, embeddings, multimodal AI, or AI evaluation frameworks.
- Understanding of FP32, FP16, BF16, INT8, tensor cores, and numerical stability.
What Excellent Looks Like:
- Can implement a model from a research paper without blindly copying code.
- Can debug why a model is not learning.
- Can explain why GPU utilization is low.
- Can identify whether a workload is compute-bound, memory-bound, or communication-bound.
- Can write clean, reproducible, and testable Python code.
- Can profile before optimizing.
- Can work across model logic, GPU execution, data pipelines, and inference systems.
- Can communicate technical findings clearly and precisely.
Ideal Candidate Profile:
- The ideal candidate is an early-career AI engineer who is not limited to using ML libraries as black boxes.
- They understand how models train, how tensors move, how GPUs execute workloads, and how performance, quality, reliability, and safety come together in real AI systems.
- The strongest candidates will show hands-on projects in model implementation, GPU acceleration, CUDA/Triton operators, distributed training, LLM evaluation, inference optimization, or research paper reproduction.
Pay Transparency
At Accellor, we believe in equitable compensation. The base salary for this position is $90,000 to $130,000 and there may be an annual bonus. The actual pay will depend on your skills, experience, and qualifications. The salary range is subject to change. Speak with your recruiter about any questions.
Benefits
We strive to offer benefits that support the diverse needs of our employees. Our package includes perks like flexible and discretionary time off, healthcare coverage for you and your loved ones, and a retirement savings plan to help you plan for the future. Additionally, we offer access to flexible spending and health savings accounts, life and AD&D insurance, and opportunities for professional development.
Accellor is proud to be an equal-opportunity employer. We do not discriminate in hiring or any employment decision based on race, color, religion, national origin, age, sex (including pregnancy, childbirth, or related medical conditions), marital status, ancestry, physical or mental disability, genetic information, veteran status, gender identity or expression, sexual orientation, or other applicable legally protected characteristics.