Machine Learning Engineer

Machine Learning Engineer at ProofofSkill · Bengaluru (Bangalore) · 3 - 5 years · ₹28L - ₹30L / yr · Raised funding · Posted 18 Sep 2026

Machine Learning Engineer

at ProofofSkill

Posted by Atharva K

3 - 5 yrs

₹28L - ₹30L / yr

Bengaluru (Bangalore)

Skills

databricks

XGBoost

Python

Machine Learning (ML)

SQL

Machine Learning Engineer (For client company)

Location: Bengaluru, India (Hybrid/Onsite)

Experience: 3–4 years

The Role

We are looking for a Machine Learning Engineer to build and productionize models that power fall detection, vitals monitoring, and predictive health insights from radar sensor data.

You will work closely with hardware, data engineering, backend, and product teams to improve model accuracy, reduce false alarms, and deploy reliable ML systems into production.

This role is ideal for someone with strong classical machine learning fundamentals who is comfortable working with messy real-world sensor data and writing clean, production-grade code.

What You'll Do

Build and optimize classical ML models such as XGBoost, ensemble models, anomaly detection, and time-series models for fall detection, vitals monitoring, and health risk scoring.
Engineer features from raw, sparse, and noisy radar signals, point-cloud data, and time-series sensor streams.
Contribute to computer vision-adjacent problems such as pose estimation, movement analysis, skeleton tracking, and activity recognition using radar data.
Build training, evaluation, and inference pipelines using Databricks.
Perform exploratory data analysis on resident, device, alert, and facility-level datasets to identify trends, edge cases, and opportunities for model improvement.
Define and own model evaluation metrics for safety-critical systems, including:
Precision
Recall
Sensitivity
Specificity
False alarm rate
Missed event rate
Detection latency
Analyze production model performance across facilities, residents, devices, and time periods.
Handle noisy real-world datasets, including:
Missing values
Label quality issues
Device variability
Sparse event data
Facility-specific patterns
Write clean, modular, well-tested Python code for feature engineering, model training, evaluation, and inference.
Deploy, monitor, and continuously improve production ML models.
Collaborate with hardware and data engineering teams to improve data quality, labeling, observability, and model reliability.

What We're Looking For

3–4 years of experience building and deploying machine learning systems in production.
Strong Python programming skills with the ability to write maintainable, testable, production-grade code.
Strong understanding of classical machine learning concepts, including:
Feature engineering
Model training
Cross-validation
Error analysis
Model evaluation
Hands-on experience with algorithms such as:
XGBoost
Random Forests
Gradient Boosting
Ensemble methods
Anomaly Detection
Time-series models
Strong SQL skills with experience analyzing large datasets using SQL, PySpark, Pandas, or Databricks.
Experience working with time-series, sensor, spatial, point-cloud, IoT, or computer vision-style datasets.
Familiarity with modern data engineering workflows using Databricks, Apache Spark, Delta Lake, or similar platforms.
Strong debugging and analytical skills with the ability to diagnose issues across data pipelines, models, and production systems.
Comfortable working in a fast-moving startup environment with ambiguity.
Strong ownership mindset with the ability to take ML models from experimentation through production deployment.

Good to Have

Experience in HealthTech, IoT, radar sensing, wearables, ambient monitoring, or safety-critical systems.

Exposure to:

Computer Vision
Pose Estimation
Skeleton Tracking
Object Tracking
Spatial Data Processing
Experience with:
MLflow
Model Registry
Feature Stores
Experiment Tracking
Model Monitoring
Experience with:
ONNX
Model Quantization
Edge Deployment
Latency Optimization
Resource-Constrained Inference
Familiarity with real-time data pipelines using:
Kafka
Spark Structured Streaming
Streaming inference architectures

Machine Learning Engineer (For client company)

Location: Bengaluru, India (Hybrid/Onsite)

Experience: 3–4 years

The Role

We are looking for a Machine Learning Engineer to build and productionize models that power fall detection, vitals monitoring, and predictive health insights from radar sensor data.

You will work closely with hardware, data engineering, backend, and product teams to improve model accuracy, reduce false alarms, and deploy reliable ML systems into production.

This role is ideal for someone with strong classical machine learning fundamentals who is comfortable working with messy real-world sensor data and writing clean, production-grade code.

What You'll Do

Build and optimize classical ML models such as XGBoost, ensemble models, anomaly detection, and time-series models for fall detection, vitals monitoring, and health risk scoring.
Engineer features from raw, sparse, and noisy radar signals, point-cloud data, and time-series sensor streams.
Contribute to computer vision-adjacent problems such as pose estimation, movement analysis, skeleton tracking, and activity recognition using radar data.
Build training, evaluation, and inference pipelines using Databricks.
Perform exploratory data analysis on resident, device, alert, and facility-level datasets to identify trends, edge cases, and opportunities for model improvement.
Define and own model evaluation metrics for safety-critical systems, including:
Precision
Recall
Sensitivity
Specificity
False alarm rate
Missed event rate
Detection latency
Analyze production model performance across facilities, residents, devices, and time periods.
Handle noisy real-world datasets, including:
Missing values
Label quality issues
Device variability
Sparse event data
Facility-specific patterns
Write clean, modular, well-tested Python code for feature engineering, model training, evaluation, and inference.
Deploy, monitor, and continuously improve production ML models.
Collaborate with hardware and data engineering teams to improve data quality, labeling, observability, and model reliability.

What We're Looking For

3–4 years of experience building and deploying machine learning systems in production.
Strong Python programming skills with the ability to write maintainable, testable, production-grade code.
Strong understanding of classical machine learning concepts, including:
Feature engineering
Model training
Cross-validation
Error analysis
Model evaluation
Hands-on experience with algorithms such as:
XGBoost
Random Forests
Gradient Boosting
Ensemble methods
Anomaly Detection
Time-series models
Strong SQL skills with experience analyzing large datasets using SQL, PySpark, Pandas, or Databricks.
Experience working with time-series, sensor, spatial, point-cloud, IoT, or computer vision-style datasets.
Familiarity with modern data engineering workflows using Databricks, Apache Spark, Delta Lake, or similar platforms.
Strong debugging and analytical skills with the ability to diagnose issues across data pipelines, models, and production systems.
Comfortable working in a fast-moving startup environment with ambiguity.
Strong ownership mindset with the ability to take ML models from experimentation through production deployment.

Good to Have

Experience in HealthTech, IoT, radar sensing, wearables, ambient monitoring, or safety-critical systems.

Exposure to:

Computer Vision
Pose Estimation
Skeleton Tracking
Object Tracking
Spatial Data Processing
Experience with:
MLflow
Model Registry
Feature Stores
Experiment Tracking
Model Monitoring
Experience with:
ONNX
Model Quantization
Edge Deployment
Latency Optimization
Resource-Constrained Inference
Familiarity with real-time data pipelines using:
Kafka
Spark Structured Streaming
Streaming inference architectures

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About ProofofSkill

Founded :

2024

Type :

Product

Size :

0-20

Stage :

Raised funding

About

N/A

Company social profiles

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Application Details

Ready to make an impact? Apply today and become part of the QX Impact team!

About Us:

Role Overview

Responsibilities:

Feature Engineering & Discovery: Design and build complex feature sets for diverse problem types, including behavioural features for churn, sensor-based lags for maintenance, and seasonal encodings for demand forecasting.
Model Selection & Optimization: Conduct systematic experimentation across diverse algorithms (e.g., XGBoost, LightGBM, Prophet, or Deep Learning) to identify the best-performing models.
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Skills & Requirements:

3+ Years of Experience: Proven track record of developing and delivering production-grade ML models across multiple domains (Sales, Finance, Manufacturing, or Supply Chain).
Mastery of the Python Ecosystem: Expert-level skills in Pandas, NumPy, Scikit-learn, and SciPy.
Advanced Algorithmic Knowledge: Deep expertise in supervised and unsupervised learning, specifically ensemble methods (Boosting/Bagging) and time-series frameworks.
Statistical Foundations: Strong grasp of hypothesis testing, probability distributions, and the mathematical principles behind model evaluation and optimization.
SQL Proficiency: Expert ability to manipulate data within consolidated database layers to create the "Silver" feature sets required for training.
Education: Bachelor’s or Master’s degree in a quantitative field (e.g., Data Science, Statistics, Mathematics, or Computer Science).
Cloud Awareness: Experience with Azure Machine Learning or similar cloud modelling environments.
Engineering Familiarity: Basic understanding of Docker, MLflow, or FastAPI for handing models off to deployment teams.

Personal Attributes:

Strong problem-solving skills with a passion for data architecture.
Excellent communication skills with the ability to explain complex data concepts to non-technical stakeholders.
Highly collaborative, capable of working with cross-functional teams.
Ability to thrive in a fast-paced, agile environment while managing multiple priorities effectively.

Competencies:

Tech Savvy - Anticipating and adopting innovations in business-building digital and technology applications.
Self-Development - Actively seeking new ways to grow and be challenged using both formal and informal development channels.
Action Oriented - Taking on new opportunities and tough challenges with a sense of urgency, high energy, and enthusiasm.
Customer Focus - Building strong customer relationships and delivering customer-centric solutions.
Optimize Work Processes - Knowing the most effective and efficient processes to get things done, with a focus on continuous improvement.

Why Join Us?

Be part of a collaborative and agile team driving cutting-edge AI and data engineering solutions.
Work on impactful projects that make a difference across industries.
Opportunities for professional growth and continuous learning.
Competitive salary and benefits package.

Application Details

Ready to make an impact? Apply today and become part of the QX Impact team!

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Minimum 5 years of experience in Data Science or Applied Machine Learning.
Strong proficiency in Python, SQL, and ML libraries (Pandas, Scikit-learn, TensorFlow, PyTorch).
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Solid understanding of query optimization, storage systems, and data pipelines.
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Description

Responsibilities

Design, build, and deploy scalable machine learning models into production systems.
Develop advanced analytics and predictive models using Python, SQL, and popular ML/DL frameworks (Pandas, Scikit-learn, TensorFlow, PyTorch).
Leverage Databricks, Apache Spark, and Hadoop for large-scale data processing and model training.
Implement workflows and pipelines using Airflow and AWS EMR for automation and orchestration.
Collaborate with engineering teams to integrate models into cloud-based applications on AWS.
Optimize query performance, storage usage, and data pipelines for efficiency.
Conduct end-to-end experiments, including data preprocessing, feature engineering, model training, validation, and deployment.
Drive initiatives independently with high ownership and accountability.
Stay up to date with industry best practices in machine learning, big data, and cloud-native deployments.

Requirements

Minimum 5 years of experience in Data Science or Applied Machine Learning.
Strong proficiency in Python, SQL, and ML libraries (Pandas, Scikit-learn, TensorFlow, PyTorch).
Proven expertise in deploying ML models into production systems.
Experience with big data platforms (Hadoop, Spark) and distributed data processing.
Hands-on experience with Databricks, Airflow, and AWS EMR.
Strong knowledge of AWS cloud services (S3, Lambda, SageMaker, EC2, etc.).
Solid understanding of query optimization, storage systems, and data pipelines.
Excellent problem-solving skills, with the ability to design scalable solutions.
Strong communication and collaboration skills to work in cross-functional teams.

Benefits

Best-in-class salary: We hire strong talent and compensate accordingly.
Proximity Talks: Meet and learn from designers, engineers, product leaders, and AI practitioners.
Continuous learning: Work with a world-class team and stay close to the latest in AI, engineering, and product development.
High-impact work: Build AI-first systems and products used at scale by global clients.

About Us

Founding ML Engineer

Posted by Saif Khan

Bhilai, Raipur

0 - 4 yrs

₹7L - ₹13L / yr (ESOP available)

Python

PyTorch

TensorFlow

PostgreSQL

Retrieval Augmented Generation (RAG)

+11 more

ABOUT

The Persona Labs is building a new kind of social platform focused on something most social products do not explicitly optimize for: helping people become real friends.

We want to help people discover interesting people around them, find meaningful common ground, start low-pressure interactions, continue promising conversations, create shared experiences, and ultimately build real-life friendships.

DISCOVER → CURIOSITY → COMPATIBILITY → INTERACTION → UNDERSTAND → IRL EXPERIENCE → FRIENDSHIP

THE AI LAYER - COMPANION INTELLIGENCE

Alongside the platform, we are building a proactive personal AI companion that learns about the user and helps them navigate this journey through personalized recommendations, suggestions, reminders, conversations, and experiences.

THE OPPORTUNITY

We are looking for a Founding ML Engineer to build the intelligence layer of the platform from the ground up. This is a 0→1 Applied AI / ML role where you will work directly with the founder and Product Engineer to turn ambiguous problems around users, relationships, recommendations and personal intelligence into working systems.

You will be expected to:

Understand the problem → identify the signals → design the intelligence system → prototype → evaluate → deploy → learn → improve.

WHAT YOU WILL BUILD & OWN

USER INTELLIGENCE

User representations, behavioural models, interests, preferences, contextual signals, and evolving understanding of the user. MEMORY Short- and long-term memory, episodic/preference/relationship memory, retrieval, relevance and updating.

RECOMMENDATION & MATCHING

People discovery, compatibility, activity/experience recommendations, and personalized ranking.

INTENT & INTEREST

Infer what the user is trying to do and learn what they care about from behaviour, not only declared interests.

RANKING

Decide what should appear first across potentially thousands of relevant people, activities or experiences.

CONTENT INTELLIGENCE

Classification, toxicity, spam, policy signals, quality, relevance, and semantic understanding.

RELATIONSHIP INTELLIGENCE

Reciprocity, interaction health, shared interests, progression, declining engagement and shared activity.

NEXT-BEST-ACTION

Determine the most useful action now: show a person, suggest a question, recommend an activity, reconnect, or do nothing.

TRUST / SAFETY INTELLIGENCE

Fake-account signals, spam, abuse, behavioural anomalies, risky interactions and moderation assistance.

COMPANION INTELLIGENCE

Use signals and outputs to help the companion decide what to say, suggest, recommend or not do.

WHAT YOUR DAY-TO-DAY LOOKS LIKE

• Translate ambiguous product problems into ML/AI system designs.

• Build models and intelligence pipelines using behavioural, relational and contextual signals.

• Develop recommendation, matching and personalization systems.

• Design memory and retrieval systems that help the companion understand the user over time.

• Build and evaluate LLM-powered and agentic workflows.

• Decide when to use traditional ML, rules, retrieval, ranking or LLMs.

• Prototype quickly, test assumptions and iterate based on real user behaviour.

• Work closely with the founder and Product Engineer to turn intelligence into product experiences.

• Design APIs and production systems that bring ML/AI capabilities into the application.

• Build evaluation, monitoring and feedback loops so the intelligence improves over time.

WHO SHOULD APPLY

• Experience: 0–4 years’ experience, including exceptional fresh graduates. Strong 1–3 year engineers and experienced 3–4 year product builders are welcome.

• Strong foundations in ML, Python, statistics and software engineering.

• Evidence of Building: Experience with AI/ML projects, recommendation systems, LLM applications or personalization is highly valued.

• Strong evidence of building: Shipped projects, research, hackathons, internships, open source or startup work.

WHAT WE LOOK FOR

MACHINE LEARNING DEPTH

Can you understand the modelling problem underneath the application?

RECOMMENDATION & PERSONALIZATION

Can you reason about relevance, ranking, cold start and behavioural signals?

AI ENGINEERING

Can you turn LLMs and agents into reliable product capabilities rather than simple API wrappers?

USER INTELLIGENCE

Can you design systems that gradually understand a person from sparse and changing signals?

SYSTEMS THINKING

Can you move from a model to a production system with APIs, data, latency, cost and monitoring?

EVALUATION MINDSET

Can you determine whether the intelligence actually helped the user?

PRODUCT JUDGMENT

Can you decide what the system should do when there is no predefined answer?

SPEED OF EXECUTION

Can you move from idea → prototype → evaluation → production quickly and responsibly?

BUILD WITH US

You will join at a stage where many of the answers do not exist yet. You will not simply implement a model someone else selected; you will help decide how the product learns to understand people.

CAREERS:

Apply with your resume, GitHub, portfolio or shipped work.

https://forms.gle/12YpUSBY2Sqs5xjp8

www.thepersonalabs.com

ABOUT

The Persona Labs is building a new kind of social platform focused on something most social products do not explicitly optimize for: helping people become real friends.

DISCOVER → CURIOSITY → COMPATIBILITY → INTERACTION → UNDERSTAND → IRL EXPERIENCE → FRIENDSHIP

THE AI LAYER - COMPANION INTELLIGENCE

THE OPPORTUNITY

You will be expected to:

Understand the problem → identify the signals → design the intelligence system → prototype → evaluate → deploy → learn → improve.

WHAT YOU WILL BUILD & OWN

USER INTELLIGENCE

RECOMMENDATION & MATCHING

People discovery, compatibility, activity/experience recommendations, and personalized ranking.

INTENT & INTEREST

Infer what the user is trying to do and learn what they care about from behaviour, not only declared interests.

RANKING

Decide what should appear first across potentially thousands of relevant people, activities or experiences.

CONTENT INTELLIGENCE

Classification, toxicity, spam, policy signals, quality, relevance, and semantic understanding.

RELATIONSHIP INTELLIGENCE

Reciprocity, interaction health, shared interests, progression, declining engagement and shared activity.

NEXT-BEST-ACTION

Determine the most useful action now: show a person, suggest a question, recommend an activity, reconnect, or do nothing.

TRUST / SAFETY INTELLIGENCE

Fake-account signals, spam, abuse, behavioural anomalies, risky interactions and moderation assistance.

COMPANION INTELLIGENCE

Use signals and outputs to help the companion decide what to say, suggest, recommend or not do.

WHAT YOUR DAY-TO-DAY LOOKS LIKE

• Translate ambiguous product problems into ML/AI system designs.

• Build models and intelligence pipelines using behavioural, relational and contextual signals.

• Develop recommendation, matching and personalization systems.

• Design memory and retrieval systems that help the companion understand the user over time.

• Build and evaluate LLM-powered and agentic workflows.

• Decide when to use traditional ML, rules, retrieval, ranking or LLMs.

• Prototype quickly, test assumptions and iterate based on real user behaviour.

• Work closely with the founder and Product Engineer to turn intelligence into product experiences.

• Design APIs and production systems that bring ML/AI capabilities into the application.

• Build evaluation, monitoring and feedback loops so the intelligence improves over time.

WHO SHOULD APPLY

• Experience: 0–4 years’ experience, including exceptional fresh graduates. Strong 1–3 year engineers and experienced 3–4 year product builders are welcome.

• Strong foundations in ML, Python, statistics and software engineering.

• Evidence of Building: Experience with AI/ML projects, recommendation systems, LLM applications or personalization is highly valued.

• Strong evidence of building: Shipped projects, research, hackathons, internships, open source or startup work.

WHAT WE LOOK FOR

MACHINE LEARNING DEPTH

Can you understand the modelling problem underneath the application?

RECOMMENDATION & PERSONALIZATION

Can you reason about relevance, ranking, cold start and behavioural signals?

AI ENGINEERING

Can you turn LLMs and agents into reliable product capabilities rather than simple API wrappers?

USER INTELLIGENCE

Can you design systems that gradually understand a person from sparse and changing signals?

SYSTEMS THINKING

Can you move from a model to a production system with APIs, data, latency, cost and monitoring?

EVALUATION MINDSET

Can you determine whether the intelligence actually helped the user?

PRODUCT JUDGMENT

Can you decide what the system should do when there is no predefined answer?

SPEED OF EXECUTION

Can you move from idea → prototype → evaluation → production quickly and responsibly?

BUILD WITH US

You will join at a stage where many of the answers do not exist yet. You will not simply implement a model someone else selected; you will help decide how the product learns to understand people.

CAREERS:

Apply with your resume, GitHub, portfolio or shipped work.

https://forms.gle/12YpUSBY2Sqs5xjp8

www.thepersonalabs.com

Senior Data Scientist

Global Wearables Tech Lead with offices in US, EU, ME and IN

Agency job

via by Gaurav Gadgil

Bengaluru (Bangalore)

3 - 6 yrs

₹30L - ₹45L / yr

PyTorch

TensorFlow

Data Science

Machine Learning (ML)

Time series

+8 more

Are you looking to work in the cutting edge area of applying data-science to help global customers get a better insight into their health? If so, read on and apply.

Role Name: Senior Data Scientist

Science Team | Full-Time | In-Office | Bangalore

The Role

The Ultrahuman Science Team builds the algorithms behind the Ring, M1 CGM, blood and urine biomarkers, and Performance Lab assessments. We are hiring a Senior Data Scientist to own those algorithms end to end: from the raw sensor signal to a model that is shipped, monitored, and trusted in users' hands.

This is a build role with real scope. In a typical month you will improve a production algorithm, root-cause a metric users are complaining about, and stand up the data pipeline the next model needs. The common thread is ownership: you take a vague question and return a working answer, without waiting to be handed scope.

What You'll Do

· Own algorithms end to end: sleep staging, activity detection, sensor-derived metrics, and health scores. You frame the problem, build the features, train and evaluate the model, and see it live

· Ship models, not notebooks: you prove a change on our own cohort before it reaches users, and a model is done only when it runs in production and you can tell how it is behaving

· Validate against reference standards: design evaluations against gold standards, reference devices, and study ground truth, and know when a result is real and when it is an artifact

· Own the data layer: cohort extraction, feature pipelines, study data, and raw sensor data, so the next model starts from clean inputs

What This Looks Like in Practice

1. Improving production algorithms - Take an existing production model like sleep staging, root-cause the failure modes against reference data, and ship a fix you can defend with numbers.

2. Building new models - Train an activity classifier on raw sensor data, design the labeled data collection that expands it, and pick the operating point so false positives never erode trust.

3. Proving it before it ships - Run a new steps algorithm against reference-device cohorts, decide with data when it is ready, and monitor how it behaves after rollout.

Who You Are

The two things we can't coach

· High ownership, end to end: you take a problem from a vague question to a shipped model without waiting to be handed scope, and you can point to something you owned from raw data all the way to production

· Hungry for more scope: you have outgrown your current role and want problems biggerthan your title, with the technical depth to be trusted with them

Also important

· You've worked with human health data: wearables, physiological signals, or clinical data.

If your experience is close but not exact, show us why you will ramp fast

· You've built at a startup: or somewhere small enough that nobody handed you clean data, clear specs, or a mature ML platform

· You work like it's 2026: coding agents and AI tooling are part of how you build every day, and you can tell which new capabilities are worth adopting

· You communicate: you can explain a model and its limits to a product manager, an engineer, or a founder, and hold your own with our scientists Core Technical Skills

· Languages and data: Python and SQL daily, comfortable working in a real codebase

· Machine learning: PyTorch or TensorFlow, scikit-learn, and gradient boosting, with the judgment to know which the problem needs

· Advanced machine learning: time series and sequence models, deep learning on continuous physiological signals, and ensembles

· Statistics and evaluation: hypothesis testing, experiment and A/B design, model evaluation, and error analysis against a reference standard

· Scale and cloud: Spark or equivalent on large datasets, and AWS, GCP, or Azure

· Production ML and MLOps: training pipelines, model versioning, deployment, monitoring, and drift detection

· LLMs and agentic systems: fine-tuning and serving models, building agentic pipelines, and using coding agents to move faster

Experience:

- 4 to 5 years building and shipping machine learning systems. We index on what you have shipped and on trajectory, not the exact number of years; if you are a little earlier but have clearly outgrown your current scope, we want to hear from you.

- Bachelor's or higher in engineering, computer science, statistics, or a related field.

How We Work and Who Thrives Here

- The Science team is small and moves fast, and much of the work has no precedent to copy.

- People do their best work here when they are energized by ambiguity, low on ego, quick to adopt a better idea no matter where it comes from, and comfortable owning something before anyone has told them how. If you need a mature data org, clean labelled datasets, and clear guardrails to thrive, this particular role will not be the right fit, and that is worth knowing up front.

What You'll Gain

· Ownership of algorithms that hundreds of thousands of people see every morning

· A dataset most scientists never get to touch: 100M+ nights of sleep and continuous physiological signals at scale

· Direct collaboration with the engineering, product, and design teams building Ultrahuman