Research Scientist – Computer Vision (Body Pose Detection)
for section headings (e.g., About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). We should map content accordingly. The instructions: Use for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). Use - for any list of duties/requirements/benefits, and
for prose. Do NOT repeat the job title as a heading (the page already shows it) and do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short
or drop it. Preserve ALL substantive information; do NOT invent or summarize away detail. Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. IMPORTANT: include a section ONLY when the posting has real, specific content for it — never emit a heading followed by filler/placeholder text (e.g. "commensurate with experience", "not specified", "N/A", "varies", "TBD") and never create Pay/Schedule/Benefits sections unless the posting actually states them. Prefer a few substantive sections over many thin ones. Output ONLY the HTML fragment — no /, no markdown code fences.
We need to decide which sections to include based on content. The posting includes:
- About Mecka AI (company description)
- The Role (description)
- What You'll Work On (detailed responsibilities)
- Who You Are (Required Background)
- Why This Role? (benefits/motivation)
- Compensation Range: $150K - $200K (Pay)
- Possibly other sections like Benefits? Not explicit benefits listed besides compensation and data advantage, pure R&D, high impact. Could treat "Why This Role?" as Benefits? But they said use Benefits section only if posting actually states them. The posting does not list explicit benefits like health insurance, etc. So we should not create a Benefits section unless there is explicit benefit info. However "Why This Role?" includes "The Data Advantage", "Pure R&D & Model Ownership", "High Impact". That's more like motivations/benefits. Could treat as a section "Why This Role?" but the allowed headings list includes About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. They didn't list "Why This Role?" as allowed, but they said use
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). The "e.g." suggests those are examples, not exclusive list. But they say "Prefer a few substantive sections over many thin ones." We could map content to these standard headings: About the role (company description), Responsibilities (What You'll Work On), Requirements/Qualifications (Who You Are), Skills (maybe part of Requirements), Benefits (Why This Role?), Pay (Compensation Range). Schedule not present, so omit.
We must not repeat job title as heading. The job title is "Research Scientist". Not to repeat.
We need to preserve all substantive information. So we need to include company description, role description, responsibilities list, requirements list, benefits/motivation, compensation.
We need to use - for any list of duties/requirements/benefits. So for responsibilities we should list bullet points. For requirements also bullet list. For benefits also bullet list.
We need to use
for prose.
We need to strip boilerplate: warning about applying multiple roles, note on applying, inclusive hiring, AI use, etc. Those are boilerplate/EEO etc. Remove them. Also remove duplicated lines. Also remove "Warning: Research Scientist positions require hyper-specific expertise..." etc. Also remove "A Note on Applying ..." etc. Also remove "Inclusive Hiring at Mecka ..." etc. Also remove "Use of Artificial Intelligence in Recruitment ..." etc. Also remove "Compensation Range: $150K - $200K" is not boilerplate; it's substantive, keep.
We need to keep "About Mecka AI" description. Keep "The Role" paragraph. Keep "What You'll Work On" sections with sub-bullets. Keep "Who You Are" required background and strong signals. Keep "Why This Role?" bullet points.
We need to decide headings: maybe:
About the role
(company description + role overview)
Responsibilities
(What You'll Work On)
Requirements
(Who You Are - Required Background + Strong Signals)
Benefits
(Why This Role?)
Pay
(Compensation Range)
We should not include Schedule.
We need to ensure we don't create empty sections.
Now we need to convert content into appropriate HTML.
Let's extract content.
Company description:
"About Mecka AI Mecka AI is building the data infrastructure layer for robotics and embodied AI. We design and operate global systems for data capture, data labeling, and hardware-enabled workflows used by leading AI labs and robotics companies to train and validate humanoid and embodied AI systems. We work closely with frontier robotics teams to bridge real-world data, simulation, learning-based systems, and deployed hardware."
That's prose.
Role overview (The Role):
"The Role While our existing perception division handles state estimation and spatial mapping, this role is dedicated to one of the most critical bottlenecks in embodied AI: full-body kinematics, human locomotion, and human-scene interaction. We are hiring a Research Scientist to architect and train proprietary foundation models from scratch focused on 3D human body tracking and articulated pose estimation. Your core mandate is twofold: building our in-house equivalents to cutting-edge 3D human body and mesh recovery architectures, and developing highly robust interaction models tailored for complex, real-world environments characterized by severe occlusions and dynamic motion. Beyond these core pillars, you will serve as a lead problem-solver for emergent perception challenges as our hardware and downstream robotics needs evolve. To achieve this, we can provide a massive, continuous stream of high-quality, proprietary ground-truth human motion data captured by our infrastructure. You will use this data advantage to train networks that surpass current public baselines, owning the complete human-scene perception loop for our data engine."
That's prose.
Responsibilities (What You'll Work On) includes several sub-sections:
- Architecting Proprietary Articulation Models
- Zero-to-One Model Development: Design, implement, and train state-of-the-art networks for 3D human pose estimation, dense full-body mesh recovery, and kinematic tracking.
- Large-Scale Distributed Training: Scale multi-view and temporal ML architectures across multi-GPU clusters to handle massive, multi-modal datasets of humans navigating and interacting with their environments.
- Loss & Architecture Innovation: Push the boundaries of current paradigms by developing novel loss functions that enforce biomechanical constraints, temporal smoothness, postural balance, and physical plausibility.
- Human-Scene Interaction (HSI) & Complex Motion Modeling
- Dynamic Scene Understanding: Build and train custom architectures capable of handling extreme motion blur, severe self-occlusion, and multi-person crowding inherent in real-world human behavior.
- Allocentric & Egocentric Tracking: Use your models to track human bodies through complex spaces, mapping foot-to-ground contact, joint torques, and environmental affordances to provide rich regularization for downstream action-conditioned robotics models (especially humanoid robots).
- Emergent Perception R&D
- Rapid Prototyping: Tackle novel, unmapped AI challenges as they arise. You will rapidly prototype and deploy new models for tasks spanning fine-grained action segmentation, intent prediction, and novel hardware sensor integrations.
- Agile Problem Solving: Pivot to resolve sudden algorithmic bottlenecks in the data engine, adapting the latest research to unblock new product capabilities for our robotics customers.
- Dense Contact & Physics-Aware Tracking
- Interaction Integration: Connect the outputs of your foundational tracking models into highly optimized pipelines that reason about physical contact surfaces, gravity, and momentum, directly bridging the gap between human video data and robotic control/locomotion policies.
We need to turn these into a list (- ) of duties. Could flatten each bullet as an
- . Might be long but okay.
Requirements (Who You Are) includes:
Required Background:
- Deep expertise in Deep Learning, 3D Computer Vision, and specifically Articulated Tracking / Human Body Pose Estimation.
- Proven experience training large-scale vision models from scratch, not just running inference or fine-tuning existing checkpoints.
- Strong theoretical and practical understanding of parametric human body models (e.g., SMPL, SMPL-X, GHUM, MHR, SOMA-X), inverse kinematics, and dense mesh estimation.
- Mastery of PyTorch and deep learning scaling frameworks.
- Experience handling and curating massive, multi-terabyte image and video datasets for training.
- Comfortable operating in a fast-paced environment where priorities can shift rapidly to capitalize on new research or hardware capabilities.
Strong Signals:
- First-author publications in top-tier venues (CVPR, ICCV, ECCV, NeurIPS) focusing on 3D human pose tracking, human-scene interaction (HSI), human motion capture, or human mesh recovery.
- Specific experience working with massive human motion and interaction datasets (e.g., AMASS, Human3.6M, EgoBody, PROX) and solving the unique optimization challenges they present.
We need to include these as list items under Requirements.
Benefits (Why This Role?):
- The Data Advantage: You will have access to a scale and quality of proprietary spatial and temporal ground truth for human motion that most academic researchers only dream of.
- Pure R&D & Model Ownership: You are not maintaining legacy systems; you are given a blank slate and the compute resources to build the state-of-the-art.
- High Impact: The kinematic priors and interaction models you architect will directly define how the next generation of embodied AI agents—from mobile manipulators to humanoid robots—learn to physically navigate, balance, and interact with the world.
Pay:
- Compensation Range: $150K - $200K
We need to output HTML fragment only.
Let's craft:
About the role
About Mecka AI Mecka AI is building the data infrastructure layer for robotics and embodied AI. We design and operate global systems for data capture, data labeling, and hardware-enabled workflows used by leading AI labs and robotics companies to train and validate humanoid and embodied AI systems. We work closely with frontier robotics teams to bridge real-world data, simulation, learning-based systems, and deployed hardware.
The Role While our existing perception division handles state estimation and spatial mapping, this role is dedicated to one of the most critical bottlenecks in embodied AI: full-body kinematics, human locomotion, and human-scene interaction. We are hiring a Research Scientist to architect and train proprietary foundation models from scratch focused on 3D human body tracking and articulated pose estimation. Your core mandate is twofold: building our in-house equivalents to cutting-edge 3D human body and mesh recovery architectures, and developing highly robust interaction models tailored for complex, real-world environments characterized by severe occlusions and dynamic motion. Beyond these core pillars, you will serve as a lead problem-solver for emergent perception challenges as our hardware and downstream robotics needs evolve. To achieve this, we can provide a massive, continuous stream of high-quality, proprietary ground-truth human motion data captured by our infrastructure. You will use this data advantage to train networks that surpass current public baselines, owning the complete human-scene perception loop for our data engine.
Then Responsibilities:
Responsibilities
- Architect
- for any list of duties/requirements/benefits, and
for prose. Do NOT repeat the job title as a heading (the page already shows it) and do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short
or drop it. Preserve ALL substantive information; do NOT invent or summarize away detail. Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. IMPORTANT: include a section ONLY when the posting has real, specific content for it — never emit a heading followed by filler/placeholder text (e.g. "commensurate with experience", "not specified", "N/A", "varies", "TBD") and never create Pay/Schedule/Benefits sections unless the posting actually states them. Prefer a few substantive sections over many thin ones. Output ONLY the HTML fragment — no /, no markdown code fences. We need to decide which sections to include based on content. The posting includes: - About Mecka AI (company description) - The Role (description) - What You'll Work On (detailed responsibilities) - Who You Are (Required Background) - Why This Role? (benefits/motivation) - Compensation Range: $150K - $200K (Pay) - Possibly other sections like Benefits? Not explicit benefits listed besides compensation and data advantage, pure R&D, high impact. Could treat "Why This Role?" as Benefits? But they said use Benefits section only if posting actually states them. The posting does not list explicit benefits like health insurance, etc. So we should not create a Benefits section unless there is explicit benefit info. However "Why This Role?" includes "The Data Advantage", "Pure R&D & Model Ownership", "High Impact". That's more like motivations/benefits. Could treat as a section "Why This Role?" but the allowed headings list includes About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. They didn't list "Why This Role?" as allowed, but they said use
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). The "e.g." suggests those are examples, not exclusive list. But they say "Prefer a few substantive sections over many thin ones." We could map content to these standard headings: About the role (company description), Responsibilities (What You'll Work On), Requirements/Qualifications (Who You Are), Skills (maybe part of Requirements), Benefits (Why This Role?), Pay (Compensation Range). Schedule not present, so omit. We must not repeat job title as heading. The job title is "Research Scientist". Not to repeat. We need to preserve all substantive information. So we need to include company description, role description, responsibilities list, requirements list, benefits/motivation, compensation. We need to use
- for any list of duties/requirements/benefits. So for responsibilities we should list bullet points. For requirements also bullet list. For benefits also bullet list.
We need to use
for prose. We need to strip boilerplate: warning about applying multiple roles, note on applying, inclusive hiring, AI use, etc. Those are boilerplate/EEO etc. Remove them. Also remove duplicated lines. Also remove "Warning: Research Scientist positions require hyper-specific expertise..." etc. Also remove "A Note on Applying ..." etc. Also remove "Inclusive Hiring at Mecka ..." etc. Also remove "Use of Artificial Intelligence in Recruitment ..." etc. Also remove "Compensation Range: $150K - $200K" is not boilerplate; it's substantive, keep. We need to keep "About Mecka AI" description. Keep "The Role" paragraph. Keep "What You'll Work On" sections with sub-bullets. Keep "Who You Are" required background and strong signals. Keep "Why This Role?" bullet points. We need to decide headings: maybe:
About the role
(company description + role overview)Responsibilities
(What You'll Work On)Requirements
(Who You Are - Required Background + Strong Signals)Benefits
(Why This Role?)Pay
(Compensation Range) We should not include Schedule. We need to ensure we don't create empty sections. Now we need to convert content into appropriate HTML. Let's extract content. Company description: "About Mecka AI Mecka AI is building the data infrastructure layer for robotics and embodied AI. We design and operate global systems for data capture, data labeling, and hardware-enabled workflows used by leading AI labs and robotics companies to train and validate humanoid and embodied AI systems. We work closely with frontier robotics teams to bridge real-world data, simulation, learning-based systems, and deployed hardware." That's prose. Role overview (The Role): "The Role While our existing perception division handles state estimation and spatial mapping, this role is dedicated to one of the most critical bottlenecks in embodied AI: full-body kinematics, human locomotion, and human-scene interaction. We are hiring a Research Scientist to architect and train proprietary foundation models from scratch focused on 3D human body tracking and articulated pose estimation. Your core mandate is twofold: building our in-house equivalents to cutting-edge 3D human body and mesh recovery architectures, and developing highly robust interaction models tailored for complex, real-world environments characterized by severe occlusions and dynamic motion. Beyond these core pillars, you will serve as a lead problem-solver for emergent perception challenges as our hardware and downstream robotics needs evolve. To achieve this, we can provide a massive, continuous stream of high-quality, proprietary ground-truth human motion data captured by our infrastructure. You will use this data advantage to train networks that surpass current public baselines, owning the complete human-scene perception loop for our data engine." That's prose. Responsibilities (What You'll Work On) includes several sub-sections: - Architecting Proprietary Articulation Models - Zero-to-One Model Development: Design, implement, and train state-of-the-art networks for 3D human pose estimation, dense full-body mesh recovery, and kinematic tracking. - Large-Scale Distributed Training: Scale multi-view and temporal ML architectures across multi-GPU clusters to handle massive, multi-modal datasets of humans navigating and interacting with their environments. - Loss & Architecture Innovation: Push the boundaries of current paradigms by developing novel loss functions that enforce biomechanical constraints, temporal smoothness, postural balance, and physical plausibility. - Human-Scene Interaction (HSI) & Complex Motion Modeling - Dynamic Scene Understanding: Build and train custom architectures capable of handling extreme motion blur, severe self-occlusion, and multi-person crowding inherent in real-world human behavior. - Allocentric & Egocentric Tracking: Use your models to track human bodies through complex spaces, mapping foot-to-ground contact, joint torques, and environmental affordances to provide rich regularization for downstream action-conditioned robotics models (especially humanoid robots). - Emergent Perception R&D - Rapid Prototyping: Tackle novel, unmapped AI challenges as they arise. You will rapidly prototype and deploy new models for tasks spanning fine-grained action segmentation, intent prediction, and novel hardware sensor integrations. - Agile Problem Solving: Pivot to resolve sudden algorithmic bottlenecks in the data engine, adapting the latest research to unblock new product capabilities for our robotics customers. - Dense Contact & Physics-Aware Tracking - Interaction Integration: Connect the outputs of your foundational tracking models into highly optimized pipelines that reason about physical contact surfaces, gravity, and momentum, directly bridging the gap between human video data and robotic control/locomotion policies. We need to turn these into a list (- ) of duties. Could flatten each bullet as an
- . Might be long but okay.
Requirements (Who You Are) includes:
Required Background:
- Deep expertise in Deep Learning, 3D Computer Vision, and specifically Articulated Tracking / Human Body Pose Estimation.
- Proven experience training large-scale vision models from scratch, not just running inference or fine-tuning existing checkpoints.
- Strong theoretical and practical understanding of parametric human body models (e.g., SMPL, SMPL-X, GHUM, MHR, SOMA-X), inverse kinematics, and dense mesh estimation.
- Mastery of PyTorch and deep learning scaling frameworks.
- Experience handling and curating massive, multi-terabyte image and video datasets for training.
- Comfortable operating in a fast-paced environment where priorities can shift rapidly to capitalize on new research or hardware capabilities.
Strong Signals:
- First-author publications in top-tier venues (CVPR, ICCV, ECCV, NeurIPS) focusing on 3D human pose tracking, human-scene interaction (HSI), human motion capture, or human mesh recovery.
- Specific experience working with massive human motion and interaction datasets (e.g., AMASS, Human3.6M, EgoBody, PROX) and solving the unique optimization challenges they present.
We need to include these as list items under Requirements.
Benefits (Why This Role?):
- The Data Advantage: You will have access to a scale and quality of proprietary spatial and temporal ground truth for human motion that most academic researchers only dream of.
- Pure R&D & Model Ownership: You are not maintaining legacy systems; you are given a blank slate and the compute resources to build the state-of-the-art.
- High Impact: The kinematic priors and interaction models you architect will directly define how the next generation of embodied AI agents—from mobile manipulators to humanoid robots—learn to physically navigate, balance, and interact with the world.
Pay:
- Compensation Range: $150K - $200K
We need to output HTML fragment only.
Let's craft:
About the role
About Mecka AI Mecka AI is building the data infrastructure layer for robotics and embodied AI. We design and operate global systems for data capture, data labeling, and hardware-enabled workflows used by leading AI labs and robotics companies to train and validate humanoid and embodied AI systems. We work closely with frontier robotics teams to bridge real-world data, simulation, learning-based systems, and deployed hardware.
The Role While our existing perception division handles state estimation and spatial mapping, this role is dedicated to one of the most critical bottlenecks in embodied AI: full-body kinematics, human locomotion, and human-scene interaction. We are hiring a Research Scientist to architect and train proprietary foundation models from scratch focused on 3D human body tracking and articulated pose estimation. Your core mandate is twofold: building our in-house equivalents to cutting-edge 3D human body and mesh recovery architectures, and developing highly robust interaction models tailored for complex, real-world environments characterized by severe occlusions and dynamic motion. Beyond these core pillars, you will serve as a lead problem-solver for emergent perception challenges as our hardware and downstream robotics needs evolve. To achieve this, we can provide a massive, continuous stream of high-quality, proprietary ground-truth human motion data captured by our infrastructure. You will use this data advantage to train networks that surpass current public baselines, owning the complete human-scene perception loop for our data engine.
Then Responsibilities:Responsibilities
- Architect
- for any list of duties/requirements/benefits. So for responsibilities we should list bullet points. For requirements also bullet list. For benefits also bullet list.
We need to use