Principal Machine Learning Engineer at Information Management Consultants, Inc.

Sydney, New South Wales, Australia -

Full Time

Start Date

Immediate

Expiry Date

01 Mar, 26

Salary

250000.0

Posted On

01 Dec, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Deep Learning, Python, CUDA, C++, PyTorch, TensorFlow, JAX, Distributed Training, GPU Optimization, Model Deployment, Data Pipelines, Experiment Tracking, Model Versioning, Reproducibility, Monitoring

Industry

Financial Services

Description

At IMC, we believe technology is the foundation of our competitive edge — and machine learning is increasingly central to how we trade. Over the past few years, we've been steadily building our machine learning capabilities: developing infrastructure, growing our in-house GPU cluster, deploying models into production, and partnering closely with quant researchers and traders to generate real impact. Now we’re expanding the team, scaling our systems, and accelerating the application of deep learning in our research and execution workflows. We're looking for a Principal Machine Learning Engineer to help shape the next phase of our platform — influencing architecture, driving best practices, and solving high-leverage problems. You’ll work alongside researchers and technologists to design the systems that power experimentation, training, and deployment of ML models — and help set the direction for how machine learning is done at IMC as we scale. If you’ve built ML infrastructure at scale elsewhere and are looking for a role where your ideas will genuinely help shape our firm’s future — we’d love to hear from you. Your Core Responsibilities: Design and build end-to-end infrastructure for training, evaluation, and productionization of ML models, working closely with our HPC engineers who manage our on-prem compute cluster Influence foundational choices around data access, compute orchestration, experiment tracking, model versioning, and deployment pipelines Partner with quant researchers to accelerate iteration cycles, tighten feedback loops, and bring models from prototype to live trading Work with researchers to adapt and deploy modern architectures — transformers, state-space models, temporal convolutions, graph neural networks — to noisy, high-frequency financial data. Explore techniques like self-supervised pretraining, representation learning, and cross-sectional modelling where they offer genuine edge Shape our approach to reproducibility, continual learning, and production monitoring across a petabyte-scale data environment Define standards that create consistency across teams and geographies; mentor engineers and influence technical culture beyond your immediate work Keep pace with developments in deep learning research and ML infrastructure; bring ideas from academia and industry into how we work — whether that's new architectures, training techniques, or tooling Your Skills and Experience: 8+ years of experience building ML platforms or infrastructure at a leading tech company, research lab, or quantitative firm A track record of designing and owning large-scale training and inference systems — not just contributing, but architecting Deep proficiency in Python, with strong experience in either CUDA or C++ Hands-on expertise with modern deep learning frameworks (PyTorch, TensorFlow, or JAX) and practical experience implementing architectures like transformers, attention mechanisms, or sequence models Strong foundation in deep learning fundamentals: optimization, regularization, loss design, and the trade-offs that matter when training at scale Experience with distributed training at scale (Horovod, NCCL) and GPU optimization (cuDNN, TensorRT) History of deploying models to production with strong observability, reproducibility, and monitoring practices Comfort working across the ML stack from data pipelines to training infrastructure to serving systems Why This Role: Build, don't inherit — You'll make foundational technology choices in a platform that's still being defined, not maintain someone else's legacy. Real investment, real backing — This is a strategic priority with resources behind it, not a side experiment. Direct impact on trading — Your infrastructure will power models that make real trading decisions in competitive global markets. Global scope — Work with teams across New York, Chicago, Amsterdam, London, Sydney, Hong Kong and beyond; define practices that can scale worldwide. Ideas over titles — IMC's culture values clarity, rigor, and collaboration. The best ideas win, regardless of where they come from. Tight coupling with research — You won't be building in isolation. Researchers and engineers work side-by-side, iterating together. #LI-DNP The Base Salary range for the role is included below. Base salary is only one component of total compensation; all full-time, permanent positions are eligible for a discretionary bonus and benefits, including paid leave and insurance. Please visit Benefits - US | IMC Trading for more comprehensive information. Salary Range $200,000—$250,000 USD About Us IMC is a global trading firm powered by a cutting-edge research environment and a world-class technology backbone. Since 1989, we’ve been a stabilizing force in financial markets, providing essential liquidity upon which market participants depend. Across our offices in the US, Europe, Asia Pacific, and India, our talented quant researchers, engineers, traders, and business operations professionals are united by our uniquely collaborative, high-performance culture, and our commitment to giving back. From entering dynamic new markets to embracing disruptive technologies, and from developing an innovative research environment to diversifying our trading strategies, we dare to continuously innovate and collaborate to succeed.

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Responsibilities

Design and build end-to-end infrastructure for ML models while collaborating with HPC engineers. Influence foundational choices around data access and deployment pipelines to enhance model performance.