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About This Role
Machine Learning Research Engineer (ScientificEngineering AI)
Urgent Hiring Requirement
Minimum Qualification: PhD in a relevant technical field.
This is an urgent requirement with an anticipated start date within 2 weeks. Priority will be given to candidates who can interview promptly and begin within two weeks of selection.
Job Summary
We are seeking a highly motivated Machine Learning Research Engineer (ScientificEngineering AI) with strong expertise in Machine Learning, Deep Learning, Computer Vision, and AI research. This role is intended exclusively for PhD graduates or candidates near completion from reputable universities.
Candidates with a strong academic research background in Machine Learning, Artificial Intelligence, Computer Vision, Data Science, Scientific Computing, Mechanical Engineering, Materials Science, Manufacturing Engineering, Applied Physics, Computational Engineering, or related fields are encouraged to apply.
Ideal candidates will combine strong ML/DL expertise with domain knowledge in mechanical engineering, materials science, manufacturing systems, physical systems, scientific computing, or simulation\-driven engineering applications.
Research experience gained during a PhD program will be considered equivalent to professional industry experience.
This is an urgent hiring requirement, and we are actively seeking candidates who can start within the next 2 weeks.
Education Requirement
PhD in Computer Science, Computer Engineering, Electrical Engineering, Artificial Intelligence, Machine Learning, Data Science, Mechanical Engineering, Materials Science, Manufacturing Engineering, Applied Physics, Computational Engineering, or a related technical field.
Candidates currently pursuing a PhD with anticipated graduation within the next 3\-6 months are also encouraged to apply.
Only PhD candidates will be considered for this role.
Candidates with only a Master's degree will not be considered.
Key Responsibilities
Design, develop, train, and optimize Machine Learning and Deep Learning models for real\-world applications.
Own the complete ML lifecycle including data collection, annotation, preprocessing, model training, fine\-tuning, evaluation, optimization, and deployment.
Develop and deploy advanced deep learning architectures including CNNs, LSTMs, ConvLSTMs, Graph Neural Networks (GNNs), Reinforcement Learning, and Transformer\-based models.
Conduct experiments, evaluate model performance, and drive continuous algorithmic improvements.
Work with large\-scale datasets for model training, validation, and testing.
Optimize and deploy AI models for scalable and efficient real\-world applications.
Translate research concepts into scalable, production\-ready AI systems.
Collaborate with cross\-functional engineering and research teams to integrate ML models into real\-world applications.
Document methodologies, experimental findings, and technical solutions.
Contribute to technical innovation initiatives and advanced AI research activities.
Required Qualifications
Strong PhD research background in Machine Learning, Deep Learning, Artificial Intelligence, Computer Vision, Data Science, Scientific Machine Learning, Computational Engineering, Applied Physics, Materials Informatics, or related areas.
Strong programming experience with Python and C\+\+.
Hands\-on experience with PyTorch, TensorFlow, Keras, Scikit\-learn, or similar ML frameworks.
Strong understanding of Machine Learning, Deep Learning, Neural Networks, Computer Vision, and AI algorithms.
Experience developing and training advanced deep learning models and architectures.
Solid mathematical foundation in linear algebra, probability, statistics, optimization, and applied machine learning.
Experience working with Linux environments, Git, Docker, and modern development workflows.
Demonstrated research experience through publications, thesis work, academic research projects, or equivalent research contributions.
Strong ability to independently research, prototype, and deploy AI solutions.
Experience applying machine learning or deep learning techniques to engineering, manufacturing, materials science, physical systems, scientific computing, simulation, or industrial applications is highly desirable.
Preferred Qualifications
Publications in leading AI, Machine Learning, Computer Science, Scientific Computing, Computational Engineering, Materials Science, or Applied Physics conferences and journals.
Experience transitioning AI/ML models from research environments into production systems.
Experience with CUDA, GPU acceleration, distributed computing, high\-performance computing (HPC), or parallel computing environments.
Experience handling large\-scale, real\-world datasets.
Familiarity with Physics\-Informed Machine Learning (PIML), Physics\-Informed Neural Networks (PINNs), scientific foundation models, digital twins, simulation\-driven AI, or engineering optimization techniques.
Experience working with data generated from CAD, CAE, CFD, FEA, multiphysics simulations, manufacturing processes, materials characterization, laboratory testing, or other engineering and scientific workflows.
Technical Skills
Python, C\+\+
PyTorch, TensorFlow, Keras, Scikit\-learn
Machine Learning and Deep Learning
Computer Vision
Reinforcement Learning
Graph Neural Networks (GNNs)
Transformer Architectures
Linux, Git, Docker
CUDA and GPU Computing
Scientific Computing and Optimization
Physics\-Informed Machine Learning (Preferred)
Engineering and Scientific Data Analysis (Preferred)
Role Details
About This Role
Research Engineers bridge the gap between research and production. They implement papers, build experiment infrastructure, optimize training pipelines, and make research prototypes production-ready. They're the engineers who make research work at scale.
The role sits at a unique intersection. You need to understand the math well enough to implement novel architectures correctly, and you need the engineering chops to make them run efficiently on distributed systems. When a research scientist has a breakthrough idea, you're the person who turns it from a notebook prototype into a training pipeline that runs on 256 GPUs.
Across the 3,823 AI roles we're tracking, Research Engineer positions make up 2% of the market. At Optimal Inc., this role fits into their broader AI and engineering organization.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
What the Work Looks Like
A typical week involves: implementing a new attention mechanism from a recent paper, profiling and optimizing a training pipeline that's bottlenecked on data loading, building evaluation infrastructure for a new benchmark, debugging distributed training issues across a GPU cluster, and pair-programming with a research scientist on their latest experiment. The work is deeply technical.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
Skills Required
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Experience with large-scale training infrastructure (FSDP, DeepSpeed, Megatron), GPU programming (CUDA, Triton), and the internals of ML frameworks (PyTorch internals, custom autograd functions) is what makes candidates stand out. The best research engineers can debug issues that span the full stack from GPU memory management to numerical precision to algorithmic correctness.
Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
Compensation Benchmarks
Research Engineer roles pay a median of $260,000 based on 434 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Optimal Inc. AI Hiring
Optimal Inc. has 3 open AI roles right now. They're hiring across Research Engineer, MLOps Engineer, AI/ML Engineer. Based in Warren, MI, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into Research Engineer roles include Software Engineer, ML Engineer, Research Intern.
From here, career progression typically leads toward Senior Research Engineer, Research Scientist, ML Architect.
This is one of the best entry points into AI research without a PhD. Build a strong engineering portfolio with ML projects, contribute to open-source ML frameworks, and demonstrate that you can implement complex ideas correctly and efficiently. The transition to Research Scientist is possible with published first-author work, which some research engineer roles support.
What to Expect in Interviews
Technical screens test both engineering skill and research understanding. Expect coding rounds with performance-critical implementations (GPU optimization, efficient data loading). Be prepared to discuss papers relevant to the team's research area and explain how you'd implement key ideas. System design questions focus on training infrastructure: distributed training, experiment tracking, and compute resource management.
When evaluating opportunities: Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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