Senior Machine Learning Engineer

Quantiphi; Atlanta (GA); On-site; USD 140,000 - 210,000; Full time

Job summary

Quantiphi is seeking a highly skilled Machine Learning Engineer to join our AI/ML team. You will own the path from experiment to production, fine-tuning models, evaluating performance, and optimizing inference for edge environments.

You will build data pipelines, design ML architectures, and curate instruction-tuning data while collaborating with cross-functional teams to deliver production-grade AI solutions for Fortune 1000 clients.

Qualifications

• Hands-on experience with fine-tuning LLMs and production deployment.
• Proficiency with Python, PyTorch, and Hugging Face ecosystem.
• Experience with vLLM, TensorRT-LLM, and quantization techniques.
• Knowledge of data pipelines, evaluation benchmarks, and ML deployment.

Responsibilities

• Model fine-tuning and evaluation.
• Data transformation and ML modeling.
• GPU and inference optimization, edge deployment.
• Build data pipelines for training and testing datasets.
• Collaborate with hardware and platform teams for integration.

Skills

LLM fine-tuning

LoRA/QLoRA

DPO

Python

PyTorch

Hugging Face

vLLM

TensorRT-LLM

CUDA profiling

Benchmarking

Data pipelines

Docker

Kubernetes

Linux

NVIDIA GPUs

Tools

Docker

Kubernetes

Linux

TensorRT

NvInfer

Job description

Quantiphi is an award-winning, AI-first global digital engineering company that helps the world’s leading Fortune 1000 organizations transform bold ideas into measurable business impact. We go beyond building innovative AI technologies - we solve the problems that matter most to our clients.

Since our founding in 2013, Quantiphi has built a proven track record of turning complex challenges into meaningful outcomes across industries.

Headquartered in Boston, with more than 4,000 professionals worldwide, we partner with global enterprises to deliver large-scale digital, cloud, and AI-driven transformation. #SolvingWhatMatters

We are an Elite and Premier partner to Google Cloud, AWS, NVIDIA, Snowflake, and other leading technology platforms, and our work has been recognized across the industry, including:

• 21 Google Cloud Partner of the Year awards in the past 10 years
• 3 AWS AI/ML Partner of the Year awards
• 3 NVIDIA Partner of the Year awards
• 3 Snowflake Partner of the Year awards
• Rated Leaders by Gartner, Forrester, IDC, ISG, Everest Group and other leading analyst firms

Quantiphi delivers First-in-class AI solutions across Life Sciences, Healthcare, Banking, Financial Services, CPG, Manufacturing, Energy, High-Tech, Telecommunications, etc., powered by cutting-edge Generative AI and Agentic AI accelerators.

We are also proud to be certified as a Great Place to Work - reflecting our commitment to our people and our culture.

Job Description:

We are seeking a highly skilled and motivated Machine Learning Engineer to join our AI/ML team. You will join a team that delivers AI agents and models into production for industrial use cases. You will own the path from experiment to production: fine-tuning, evaluation, inference optimization and benchmarking.

Key Responsibilities:

Model fine-tuning and evaluation

• Fine-tune Nemotron, Gemma, or any other open-source models using techniques such as LoRA, QLoRA, RLHF, DPO, and SFT (Supervised Fine-Tuning), for tasks such as tool calling, RAG and domain-specific responses.
• Build data pipelines for cleaning, curation, synthetic data generation, and train, validation and golden test sets.
• Conduct prompt engineering and prompt tuning to optimize LLM/SLM agent behavior for specific use cases
• Build evaluation frameworks covering function-calling accuracy, retrieval precision, task benchmarks and the accuracy-versus-latency trade-off.
• Iterate on agent performance through systematic experimentation and hyperparameter optimization

Data Transformation & ML Modeling

• Develop and maintain end-to-end data transformation pipelines for preparing training datasets, including data cleaning, augmentation, tokenization, and formatting
• Design ML models and training architectures aligned with agent task objectives
• Curate and manage high-quality instruction-tuning and RLHF datasets for agent training
• Apply data-centric AI methodologies to improve model quality through better data rather than solely model changes
• Build and validate benchmarks and evaluation datasets to track model improvements

GPU & Inference optimization, Edge Deployment

• Architect and implement ML model deployment pipelines for edge environments (IoT devices, embedded systems, on-premise hardware)
• Profile and optimize LLM inference for TTFT, end-to-end latency, throughput and GPU memory.
• Tune vLLM and TensorRT-LLM deployments, including prefix caching, KV-cache configuration, attention backends, batching and quantization (FP8 / FP4 / NVFP4).
• Optimize for NVIDIA GPUs and edge platforms (Blackwell-based, e.g., Jetson Thor), working within memory-bandwidth and hardware-feature constraints.
• Address latency, memory, and compute constraints for real-time inference on edge devices
• Apply knowledge distillation, pruning, and quantization techniques to reduce model size while maintaining performance
• Run load, concurrency and accuracy regression tests every release cycle, and document what worked and what did not.
• Collaborate with hardware and platform teams to ensure seamless integration of AI models into edge products

Required Skills & Qualifications:

Core technical skills (must-have)

• LLM fine-tuning: Hands-on experience with SFT and parameter-efficient methods (LoRA / QLoRA), plus preference optimization (e.g., DPO) where relevant. You should have taken fine-tuned models from experiment to production.
• ML stack: Strong Python, PyTorch and the Hugging Face ecosystem (Transformers, PEFT, TRL, Accelerate).
• LLM inference and serving: Practical experience with vLLM, TensorRT-LLM or similar. This includes prefix caching, KV-cache configuration, batching, attention backends and quantization (FP8 / FP4 / NVFP4).
• GPU architecture and profiling: CUDA fundamentals, Nsight or similar profilers, and the ability to diagnose memory, bandwidth and compute bottlenecks.
• Performance benchmarking: Ability to run load, concurrency and accuracy regression tests. You should measure TTFT, end-to-end latency and throughput, and weigh the accuracy-versus-latency trade-off.
• Evaluation design: Building benchmarks and golden datasets for LLMs and agents. Metrics include function-calling accuracy, retrieval precision and task success rate.
• Data pipelines: Data curation, transformation, synthetic data generation, and train / validation / test splits.
• Deployment and infrastructure: Docker, Kubernetes and Linux-based deployments, with familiarity with cloud or on-prem GPU environments.

Customer-facing and delivery skills (must-have)

• Able to present benchmark results and recommendations to technical and business stakeholders, and to translate customer outcomes into model and infrastructure requirements.
• Able to write technical documentation and operational guidance for handover, and to turn configurations and learnings into reusable accelerators.
• Able to work with architects and other senior technical leads on design and best practices.
• Able to coordinate with SME's on roadmap, support and issue escalation.
• Strong communication skills and comfort working embedded with customers.

Nice-to-have

• Direct experience with NVIDIA Nemotron, NeMo, NIM or NeMo Guardrails.
• Experience with edge NVIDIA platforms (Jetson-class) and their constraints, such as memory bandwidth and missing hardware features.
• RAG, agentic frameworks (LangChain, LlamaIndex, AutoGen) and tool / function calling.
• NL-to-SQL or small language model (SLM) evaluation.
• Distributed training (DeepSpeed, FSDP), custom kernels, and experiment tracking (MLflow, W&B).
• Async and non-blocking service design (e.g., FastAPI, asyncio) for LLM applications.
• Open-source contributions, publications or talks.

What's in it for YOU at Quantiphi?

• Join one of the world's fastest-growing AI-first digital engineering companies and make a real impact at scale.
• Lead and collaborate with a high-energy team of talented, driven individuals solving complex, meaningful challenges.
• Work with Fortune 500 companies and disruptive innovators in a research-driven environment with 60+ patents.
• Stay ahead of the curve by gaining hands-on experience with cutting-edge AI, ML, data, and cloud technologies while continuously upskilling.