Senior Machine Learning Engineer
Evolution Cloud Services (EVOCS)United States · Posted 4 months agoDescription
EVOCS OVERVIEW
EVOCS was founded with a clear purpose: to help businesses operate more effectively, solve complex challenges, and create opportunities for growth through practical expertise and technology solutions.
As an IT consulting firm, we work with our clients to understand their needs, identify the right technologies, and deliver solutions that improve performance and support their business objectives.
Today, EVOCS is a trusted technology partner to a growing number of organizations and industry leaders. Our team combines technical expertise, business understanding, and a commitment to quality to deliver effective solutions and build lasting client relationships based on responsiveness, consistency, and results.
đŻ Role Overview
As a Senior Machine Learning Engineer, you will be the person we trust with the training side of our AI work. Youâll decide what to build, how to build it, and whether to build it at all. You will be responsible for the quality of the models we ship: the data they learn from, the pipelines that produce them, and the judgment calls that separate a useful model from an expensive one. Youâll be mentoring engineers whoâve never watched a loss curve diverge and felt something.
𧩠What you will do
In this role, you will:
- Be the primary person responsible for the data piece of our AI initiatives. This could be called the unglamorous stuff that decides whether the model works, but this is your passion. You put the enthusiast in MLE.
- Build and maintain training and retraining pipelines in Azure AI Foundry. Weâre talking working on the model catalog, fine-tuning workflows, deployment, drift monitoring, and closing the loop when production data reveals the eval set was lying to you.
- Make the real model design calls because you know best which way to go: full fine-tune vs. LoRA/QLoRA vs. DPO vs. âbetter prompting would save us three weeks.â Know when not to train.
- Run hyperparameter work that isnât a grid search copied from a 2021 Medium post.
- Operate distributed training setups and know what breaks at scale. Pick your poison: FSDP, DeepSpeed, Megatron, accelerate, etc.
- Design eval harnesses that catch whatâs actually wrong, with a skeptical eye on benchmark contamination.
- Ship models into production as the load-bearing piece of the product, not a feature slapped on the side.
- Mentor engineers who can call an inference endpoint but have never trained one themselves.
đ§ What you will bring
The top candidate will have the following skills:
- 5+ years of ML engineering experience, with meaningful time spent fine-tuning transformer models end-to-end. Weâre not talking notebook demos, we mean real runs with real eval harnesses where you worked through and found the problems and fixed them.
- Strong Python and PyTorch, plus fluency with the Hugging Face stack (transformers, datasets, accelerate, peft, trl). Bonus for JAX; extra bonus for having read a CUDA kernel and not flinched.
- Youâve already built or seriously operated a distributed training setup and know how to set that up.
- Azure AI Foundry experience (or strong Azure ML adjacent and willingness to get deep), plus SQL and at least one data pipeline tool (dbt, Airflow, Dagster, or Spark. Weâre not religious).
- Experiment tracking discipline (W&B, MLflow, or a spreadsheet you defend philosophically) and the usual engineer stuff. You should know Git, Docker, and have the ability to actually ship.
- Fluent in English (written and spoken) â bilingual or near-native level
- Strong interpersonal and communication skills â this is a client-facing role that involves frequent interaction via email, calls, and meetings
Ideally you haveâŠ
- Run quantized models locally â GGUF, GPTQ, AWQ, MLX â and know what K-quants are and why Q4_K_M is usually the sweet spot.
- Familiarity with the whisper.cpp / flash-moe universe â efficient inference on hardware that shouldnât be able to do that. (Spoiler: our projects will go here.)
- A strong take on MoE routing, speculative decoding, or why KV-cache management is more interesting than it has any right to be.
- RLHF, DPO, or preference data curation experience.
#LI-DNI
Pay Range for jobs in the US.
đ„ Our Values
We are privileged to serve our loyal customer base in our mission to build lasting relationships with our clients based on trust and mutual success. We strive to deliver exceptional quality and consistency through a white-glove approach. By empowering businesses with tailored solutions and insights, we help them achieve their goals and navigate the ever-evolving tech landscape.
The values we live by:
- Customer-centric Solutions
- Innovation & Excellence
- Integrity & Transparency
- Data-driven Decision Making
đ Need to Know
The posting will be active for a minimum of 3 days. The active posting will continue to extend by 3 days until the position is filled.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or protected veteran status, or any other legally protected basis, in accordance with applicable law.
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