cygnisai company
### Job Description: Machine Learning Engineer
**Company Overview**
CygnisAI (cygnisai.com) is a pioneering AI startup transforming heavy industry with our patented Adaptive Inference Engine. We empower industrial leaders to achieve operational excellence, decarbonization, and multimillion-dollar profit improvements by conquering process variability in sectors such as refining, steel, cement, chemicals, minerals processing, biomass power generation, and pharmaceuticals. Founded by Dr. Amit Gupta, a renowned global expert in industrial AI with patented breakthroughs in process optimization, our mission-driven team tackles "impossible" challenges using transparent, physics-informed AI solutions. We combine visionary innovation with decades of hands-on expertise from advisors like former CEOs and site heads in power and chemical sectors. Join our founding team to innovate at the intersection of AI, software engineering, and real-world industrial impact, where sustainability and profitability align seamlessly.
**Position Summary**
We are seeking a motivated Machine Learning Engineer with strong software development skills to join our founding team. This entry-level role (0-2 years of experience) is perfect for recent graduates or early-career professionals eager to blend machine learning with software engineering. Under the guidance of experienced mentors, you'll support the development and deployment of our Adaptive Inference Engine—an AI control layer for operational excellence and decarbonization. You'll contribute to practical projects like data processing for process optimization, basic model training for variability management (e.g., in feedstocks or emissions), and assisting with system integrations. This full-remote position emphasizes hands-on learning, collaboration, and growth in a supportive startup environment, helping you build skills while contributing to real-world industrial solutions.
**Key Responsibilities**
- Assist in developing and maintaining scalable software components for our AI platform, such as integrating ML models into production systems for industrial applications (e.g., helping with data pipelines for variability in processes like emissions control).
- Support the training, fine-tuning, and evaluation of machine learning models using foundational techniques and frameworks, working with industrial datasets under team guidance.
- Contribute to building software features, including data pipelines, APIs, and automation code, while learning best practices for clean, maintainable, and efficient code.
- Help with deploying AI solutions, using tools like Docker for containerization and assisting in integrations with systems like data historians or sensors.
- Participate in data preprocessing, analysis, and visualization using libraries like NumPy, Pandas, and ML frameworks (e.g., scikit-learn or PyTorch basics), while following team standards for version control (e.g., Git), testing, and debugging.
- Collaborate with the team, including domain experts, to test and iterate on solutions, gaining exposure to real-world scenarios in industries like refining or power generation.
- Support optimization tasks, with opportunities to learn about explainable AI, performance in variable conditions, and software best practices.
- Document your work and contribute to team discussions to foster knowledge sharing and continuous improvement.
**Required Qualifications**
- Bachelor's or Master's degree in Computer Science, Software Engineering, Machine Learning, Data Science, Information Technology, or a similar field. Recent graduates with relevant academic projects, internships, or self-study are encouraged to apply.
- 0-2 years of relevant experience (including internships, academic projects, open-source contributions, or personal portfolios in ML or software development).
- Proficiency in Python and basic software engineering principles, such as programming, data structures, algorithms, and debugging.
- Basic knowledge of machine learning concepts (e.g., supervised/unsupervised learning, introductory neural networks, model evaluation) and familiarity with frameworks like scikit-learn or PyTorch.
- Familiarity with version control systems (e.g., Git) and basic software practices.
- Strong analytical and problem-solving skills, with an eagerness to learn and adapt in a fast-paced, technical startup environment.
- Good communication and collaboration skills for working in a multidisciplinary team.
**Preferred Skills**
- Exposure to deployment tools such as Docker for containerization or cloud platforms (e.g., AWS, Azure, GCP).
- Some understanding of industrial processes or applications, such as from coursework or projects in areas like energy optimization.
- Experience with building simple data pipelines or APIs for ML tasks, including familiarity with databases (e.g., SQL/NoSQL) for data handling.
- Interest in real-time systems, computer vision (e.g., basic OpenCV), or handling data variability.
- Contributions to open-source projects, GitHub repositories, or portfolios showing ML-software integration.
- Familiarity with data visualization tools (e.g., Matplotlib) or time-series data.
- Passion for sustainable technologies, like AI for decarbonization.
**What We Offer**
- Competitive entry-level salary, plus equity grant.
- Direct mentorship from our Founder & CEO (with 15+ years in oil & gas AI and patented innovations) and seasoned advisors with 40+ years of leadership in power generation and chemical processes.
- Opportunity to work on patented, breakthrough technologies with global impact, including projects delivering multimillion-dollar ROI for clients in heavy industry.
- Full remote work environment with a focus on work-life balance.
- Professional development support, such as access to online courses (e.g., Coursera ML specializations), conferences on industrial AI, and training in advanced tools like RL or physics-informed modeling.
- Access to state-of-the-art computing resources for AI development.
**How to Apply**
To apply, use the "Apply" or "Easy Apply" button on LinkedIn. Please include your resume, a short cover letter highlighting your relevant experience (e.g., ML projects, software skills, or related background), and links to any portfolios, GitHub repositories, or projects in your application. Applications are reviewed on a rolling basis, and we encourage diverse candidates from all backgrounds to apply.
