EU engineers, ready to place with your US clients

Pre-screened on AI. Remote B2B contracts. View 5 full profiles free — AI score, skills report, interview questions included.

Executive Summary

The candidate is a highly capable applied AI researcher and engineer whose profile closely matches the scientific and technical depth required for this founding engineer role. With a PhD, multiple publications in top robotics/ML venues, and experience spanning academia, deep-tech industry, and an early-stage startup, they bring strong credibility and breadth. Their recent work on language-grounded generative models and multimodal policies is directly aligned with the company's text and image generation focus. The primary risks are the absence of demonstrated cloud infrastructure experience, limited evidence of engineering team leadership, and a predominantly research-oriented career trajectory that may require acceleration toward product velocity. Overall, the candidate represents a strong FIT candidate who warrants a technical interview with targeted probing on deployment, leadership, and product mindset.

Top Strengths

✓PhD-level theoretical foundation in ML with a mathematics undergraduate background — directly satisfying the role's preferred academic qualification
✓Rare combination of research excellence (IEEE T-RO, RA-L publications) and applied engineering (SLAMcore, early-stage startup) demonstrating end-to-end delivery capability
✓Hands-on experience with multimodal and language-conditioned generative models (LLMs + Decision Transformers paper, Horizon PILLAR project) highly relevant to the role's text/image generation focus
✓Active open-source contributor and independent project initiator (OpenBallBot-RL), showing entrepreneurial drive aligned with a founding engineer profile
✓Broad technical stack (Python, PyTorch, JAX, C++, ROS, Docker, MuJoCo) and ability to work across the full ML stack from theory to simulation to deployment

Key Concerns

!Absence of cloud infrastructure experience (AWS/GCP/Azure) is a notable gap for a role requiring full lifecycle ownership including scalable deployment — must be probed in interview
!No documented team leadership or engineering management experience may be a risk for the 'lead and mentor engineers' dimension of this founding role

Culture Fit

74%

Growth Potential

High

Salary Estimate

$90,000 - $130,000 (European-based candidate on B2B contract; likely to negotiate within the stated range, potentially on the lower end given research rather than product-company compensation norms)

Assessment Reasoning

The candidate is assessed as FIT with an overall score of 82. They satisfies the most critical and differentiating requirements of the role: a PhD in a directly relevant field, a strong track record of delivering AI systems (peer-reviewed publications, open-source projects, industry engineering roles), hands-on expertise in Python/PyTorch/JAX, and direct experience with multimodal and language-conditioned generative models that align with the company's text/image generation mission. They meets or exceeds the 3–7 year experience threshold with approximately 9+ years of relevant experience. Their work at SLAMcore and an early-stage startup demonstrates they can operate in fast-moving, resource-constrained environments. The missing skills — primarily cloud infrastructure and team leadership — are notable but not disqualifying for a founding hire, as cloud skills can be acquired quickly by a strong engineer, and leadership potential can be assessed in interview. Their mathematics background, research rigor, and open-source initiative suggest high growth potential toward the C-level trajectory the company envisions. The B2B/remote structure also accommodates their France-based location. A technical interview focusing on deployment experience, leadership philosophy, and generative AI specifics is recommended before final offer.

Interview Focus Areas

Cloud infrastructure and MLOps: assess depth of experience with AWS/GCP/Azure, containerization at scale, model serving, and monitoring pipelinesTeam leadership and mentorship: explore any informal leadership, project ownership, or mentoring experiences from postdoc or startup rolesText and image generation systems: probe familiarity with diffusion models, transformer-based generation, and fine-tuning LLMs for production use casesStartup mindset and ambiguity tolerance: assess comfort with fast iteration, wearing multiple hats, and making architectural decisions with limited informationC-level ambition: evaluate long-term career goals and appetite for growing into a CTO/technical leadership role

Code Review

GoodSenior Level

No code sample was provided directly in the application, limiting direct assessment. However, the candidate maintains an active GitHub presence with original open-source projects (OpenBallBot-RL, QD benchmarks) and has a demonstrated history of research implementations using PyTorch, JAX, and C++ in complex domains. Based on indirect evidence, code quality is estimated as Good with a Senior-level proficiency, but a technical coding interview is strongly recommended to validate this.

PythonPyTorchJAXFlaxC++OpenCVMuJoCoPyBulletROSROS2DockerGit

+GitHub profile (salehiac) exists and includes open-source projects such as OpenBallBot-RL, demonstrating initiative and ability to deliver standalone, documented research code
+Use of modern ML frameworks (PyTorch, JAX/Flax) and simulation tools (MuJoCo, PyBullet) indicates strong technical depth and current tooling knowledge

-No code sample was submitted as part of this application, so direct code quality assessment is impossible; evaluation is inferred from GitHub presence and resume claims

Experience Overview

11y total · 9y relevant

The candidate is a highly credentialed AI researcher with a PhD and over 9 years of relevant ML experience spanning computer vision, reinforcement learning, multimodal models, and language-grounded systems. Their publication record in IEEE T-RO and IEEE RA-L, combined with hands-on engineering at SLAMcore and an early-stage startup, demonstrates the rare ability to straddle research and applied engineering. The primary gaps are in cloud infrastructure, production-scale MLOps, team leadership, and direct experience with text/image generation pipelines targeted by the role.

Matching Skills

PhD in AI/ML-adjacent field (Computer Vision, Machine Learning)Deep Learning and neural architectures (Neural ODEs, Meta-Learning)Reinforcement Learning (RL, Evo-RL, Quality-Diversity)Multimodal models (language-conditioned generative models, multimodal policies)Python programmingPyTorch and JAX/FlaxComputer Vision (pose estimation, segmentation, SLAM)Model training and fine-tuningLLM integration (language grounding, LLMs + Decision Transformers)Research publication track record (IEEE T-RO, IEEE RA-L, GECCO, IROS, L4DC)Open-source contributions (OpenBallBot-RL, QD-suite benchmarks)Strong mathematics background (BSc Pure Mathematics)Robotics and simulation (MuJoCo, PyBullet, ROS/ROS2)Physics-informed systems and model-predictive controlAcademic peer review service

Skills to Verify

Explicit LLM fine-tuning / RLHF / instruction-tuning experienceCloud infrastructure (AWS/GCP/Azure) - not mentionedMLOps pipelines and production deployment at scaleText and image generation systems (diffusion models, GANs for generation)Demonstrated team leadership or engineering management experienceModel monitoring and production lifecycle at scale

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