Senior Machine Learning Engineer (Reinforcement Learning/Quantization/GPU) - Between $200-400K + equ

Job Description

Machine Learning Engineer (Reinforcement Learning/Quantization/GPU), Multiple roles (Between $200-400K DOE + equity)

\n

\n

Machine Learning Engineer sought for a well-funded, highly successful defense AI scaleup who bring state of the art AI to the tactical edge. They are a talented team of engineers and researchers who perform the full stack of model training to improve models every day.

\n

\n

The company is developing advanced AI systems that can operate directly at the edge, bringing high-performance AI into demanding real-world environments without relying on constant cloud connectivity. Its technology enables sophisticated models to run securely across laptops, embedded devices, robotic platforms and other hardware, with models optimized for specific applications and operational requirements.

\n

\n

We are looking for Machine Learning Engineers to build the foundations of the research organization. Success models cover code for data acquisition, data processing, high-performance kernels, compression and much more. Bonus points if you've worked within a relevant defense AI, aerospace, edge AI, airgapped environment.

\n

\n

The ideal candidates will have experience building libraries and owning codebases within one of the following (or more):

\n

\n
• Large scale evaluations
\n
• Distributed model training environments;
\n
• Complex training architectures (distillation, RL, etc.);
\n
• Specialized reinforcement learning gyms
\n
• Compute and cluster management
\n
• Kernel Development
\n
• ML at the edge
\n

\n

\n

Offering a base of between 200-400K depending on experience + generous equity, fully remote working in the US and other benefits.

\n

\n

We have a strong preference for candidates with an active clearance to work on projects related to US Space Force, DoW, DoD etc.

\n

\n

Key Skills

\n

\n

Machine Learning Engineering, MLE, Large Language Models, LLM, Foundation Models, Model Training, Distributed Training, Distributed Systems, Data Processing, Data Pipelines, Scalable Data Processing, Training Infrastructure, ML Infrastructure, ML Systems, Model Infrastructure, Model Serving, Parallel Computing, GPU Computing, CUDA, Compute Kernels, High-Performance Computing, HPC, Reinforcement Learning, RL, Model Distillation, Quantization, Model Compression, Cluster Management, Model Evaluation, MLOps

\n

\n

Disclaimer:

\n

Attis Global Ltd is an equal opportunities employer. No terminology in this advert is intended to discriminate on any of the grounds protected by law, and all qualified applicants will receive consideration for employment without regard to age, sex, race, national origin, religion or belief, disability, pregnancy and maternity, marital status, political affiliation, socio-economic status, sexual orientation, gender, gender identity and expression, and/or gender reassignment. M/F/D/V. We operate as a staffing agency and employment business. More information can be found at attisglobal.com.

\n