Machine Learning Engineer jobs in Bengaluru, Karnataka
- Spector.aiBengaluru, Karnataka
- Experience developing machine learning models for supervised, unsupervised, ranking, or other relevant applications of machine learning.
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- CoinbaseBengaluru, Karnataka
- 2+ years of professional experience in machine learning and software engineering, with experience shipping production-grade ML services.
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- Siemens HealthineersBengaluru, Karnataka
- Anomaly Detection: Build robust algorithms to detect silent deviations in machine performance from high-frequency sensor and log data.
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- advance technologiesBengaluru, Karnataka
- Health insurance
- Provident Fund
- Work on advanced AI systems for object detection, tracking, and classification in real-world environments.
- Train and optimize models on large datasets.
- Data Collection InfotechBengaluru, Karnataka
- The candidate should be responsible for all kinds of image processing using deep learning/machine learning algorithm and computer vision.
- Volvo GroupBengaluru, Karnataka
- As our first spearhead in ML Engineering you will be part of designing and applying our MLOps solution and journey – bridging data science and software…
- SarvamBengaluru, Karnataka
- The team’s roadmap evolves with the field; we want engineers who are comfortable with that.
- Work alongside researchers, engineers, builders, and business…
- Fox CorporationBengaluru, Karnataka
- At least 3-7 years of experience in machine learning, applied data science, or related fields, with a strong focus on recommendation systems or personalization.
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- Cognite - AI for IndustryBengaluru, Karnataka
- 3–6 years of industry experience in software engineering with a strong focus on machine learning, or vice versa.
- OT dataset exposure is a plus.
- paypalBengaluru, Karnataka
- Health insurance
- Paid time off
- Lead the development and optimization of advanced machine learning models.
- This job will lead the design, development, and implementation of advanced machine…
- paypalBengaluru, Karnataka
- Health insurance
- Paid time off
- Lead the development and optimization of advanced machine learning models.
- This job will lead the design, development, and implementation of advanced machine…
- RemoteStarBengaluru, Karnataka
- Solid foundation in machine learning principles and statistical techniques.
- 2-4 years of experience in machine learning or applied AI roles.
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- TAC SecurityBengaluru, Karnataka
- — Ensures software applications are built with quality while conforming to corporate and regulatory processes and practices by.
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- IdeapokeBengaluru, Karnataka
- We are looking for an expert in machine learning to help us extract value from our data.
- Proficient in Python and basic libraries for machine learning such as…
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- RZR Global Inc.Bengaluru, Karnataka
- Experience with machine learning techniques such as regression, classification, and clustering.
- 1-3 years of professional experience in machine learning,…
- SandiskBengaluru, Karnataka
- Strong expertise in statistical analysis and machine learning techniques.
- 3–4+ years of experience in data science or machine learning pipeline.
Job Post Details
Job details
Job type
- Full-time
Location
Full job description
Role Description
We are seeking an experienced machine learning engineer to join our seasoned founding team to drive the development and innovation of our ML platform. Ideal candidates bring extensive experience in building the next generation of machine learning models and its training and serving infrastructure for the Spector.
This role requires a hands-on engineer who is passionate about our mission, thrives in a startup environment, and is committed to pushing the boundaries of what ML and GenAI can achieve in industrial resilience.
You will:
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Create ML models which help drive value for users and our company.
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Build scalable ML model training and serving infrastructure.
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Evaluate the technical tradeoffs of every decision.
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Perform code reviews and ensure exceptional code quality.
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Iterate quickly without compromising quality.
Must Have:
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Bachelor's Degree in a relevant technical field such as computer science and 3+ years of post-Bachelor’s machine learning experience; or Master’s degree in a technical field + 2+ year of post-grad machine learning experience; or PhD in a relevant technical field + 1 years of post-grad machine learning experience.
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Experience developing machine learning models for supervised, unsupervised, ranking, or other relevant applications of machine learning.
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Strong understanding of machine learning approaches and algorithms.
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Experience working with machine learning frameworks such as TensorFlow, PyTorch, Spark ML, scikit-learn, or related frameworks.
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Ability to work with both internal and external partners.
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Strong collaboration and mentorship skills.
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Entrepreneurial Mindset: Highly self motivated and adaptable with a passion for innovation, problem-solving, and making a meaningful impact in a startup environment.
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Willingness to take ownership and drive projects from concept to implementation.
Preferred to have:
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Experience in on-prem ML model deployment and observability.
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Experience in building and deploying ML models for real time time series data like sensor/IoT data.
About Spector.ai
Spector.ai is a well-funded, fast-paced, innovative seed-stage startup focused on a mission to solve the $1.5 trillion challenge of industrial asset reliability. Spector is building an AI-first industrial agent platform designed to transform plant reliability and performance from reactive to autonomous operations. By combining machine learning and domain-specific industrial AI Agents, Spector.ai enables real-time diagnostics, root cause analysis, and actionable recommendations at scale. The platform extracts insights from complex industrial data including unstructured documentation and live sensor streams reducing false positives, shortening time to resolution, and scaling expertise without reliance on data scientists.
We are rapidly growing and looking for an experienced, self-driven DevOps Engineer to join our core team. This is a unique opportunity to shape our infrastructure, tooling, and deployment practices from the ground up, ensuring we can scale effectively and reliably as we move toward our next stage of funding and growth.