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1. Land an AI Research Engineer Role at Frontier and Emerging AI Labs
AI Research Engineers are the builders behind frontier AI systems. Part scientist, part engineer, you'll implement cutting-edge architectures, optimize training at massive scale, and push the boundaries of what's possible.
The bar is extraordinary. The rewards are extraordinary. $250K-$600K+ total compensation. World-changing impact.
This is your roadmap.

Who This Is For?
For ML engineers, researchers, and PhDs who want to:
✓ Transition from academia or industry into elite AI research labs
✓ Master the unique interview format: math quizzes, ML coding, paper discussions
✓ Navigate the 8-12 week interview gauntlet at OpenAI, Anthropic, DeepMind
✓ Position yourself as a "Full-Stack AI Research & Engineering" candidate

Select Your Research Engineer Coaching preference: ​​→ Book a Discovery Call ​
2. What Clients Say?
"The mock interviews for the DeepMind quiz round were invaluable. Every topic was covered."
- Jason, ML Scientist, USA

"Sundeep's feedback on my research presentation transformed how I communicate technical work."
- Michael, AI Research Scientist, USA

"Coming from academia, I had no idea how to position myself. The strategy session changed everything."
- Avni, AI Research Engineer, USA
3. Why Brilliant Engineers Fail Research Engineer Interviews?
Research Engineer interviews are unlike anything in tech. They demand the theoretical intuition of a physicist, the systems capability of an SRE, and the research taste of a PhD advisor. Here's what's actually blocking you:

The Math Quiz Trap
  • You've been coding for years, but can you derive the backpropagation equations on a whiteboard?
  • Explain why the Hessian's eigenvalues determine optimization stability? 
  • DeepMind's oral quiz round fails 60%+ of candidates on "undergraduate" fundamentals they've forgotten since school.
  • Cost: One failed quiz round = 6-12 months before you can reapply

ML Coding ≠ LeetCode
  • Standard SWE prep won't help when you're asked to implement Multi-Head Attention from scratch in 45 minutes, or
  • Debug a training loop where the loss is flat but the code compiles.
  • These interviews test deep PyTorch intuition and ML debugging skills that most engineers never develop.
  • Cost: Average 3.2 failed interview loops before getting positioning right

Wrong Company Fit
  • OpenAI wants pragmatic scalers. Anthropic wants safety-first architects. DeepMind wants academic rigorists.
  • Each lab has a distinct "cultural phenotype" and interview style.
  • Preparing generically means failing specifically.
  • Cost: $30K-$50K/month in delayed compensation while you figure this out

→ Bottom Line
  • Every month you delay costs $30K-$50K in lost Research Engineer compensation.
  • Three months = $90K-$150K. Six months = $180K+.
  • The interviews are hard. Going in unprepared is expensive.
4. Who I Work With?
Research Engineers occupy a unique space - rigorous enough for research, pragmatic enough to build.

The clients who excel in my RE coaching share these qualities.
  • You make research real. You're the person who takes a paper and turns it into working code. You find satisfaction in the craft of implementation - debugging distributed training runs, optimizing memory usage, making experiments reproducible.

  • You're fluent in research and engineering. You can read a paper, understand the math, and translate it into PyTorch or JAX. You're equally comfortable discussing attention mechanisms and debugging CUDA errors.

  • You care about infrastructure. You understand that great research requires great tooling. You think about experiment tracking, data pipelines, and compute efficiency - not just model architecture.

  • You're comfortable with ambiguity. Research doesn't come with clear specs. You can take a vague research direction and figure out what needs to be built, what experiments to run, and how to validate results.

  • You collaborate across boundaries. You work effectively with researchers who think in theory and engineers who think in systems. You translate between worlds and make teams more productive.

  • You're patient with hard problems. You've spent days debugging a training run or tracking down a subtle numerical issue. You don't give up when things don't work immediately - you systematically narrow down the problem.

If you're targeting Research Engineer roles at frontier labs like Anthropic, DeepMind, OpenAI, Google and this resonates, let's talk.
5. Choose Your Path to Research Engineer
1. Research Engineer Career Guide ($79)
The complete 50+ page roadmap to crack Research Engineer interviews independently.

What's Inside:
✓ 12-week intensive preparation roadmap
✓ Math foundations refresher (Algebra, Calculus, Probability)
✓ ML coding questions with solutions (Transformer, VAE, PPO)
✓ Company-specific breakdowns: OpenAI, Anthropic, DeepMind interview processes
✓ Research discussion frameworks, paper analysis templates
✓ 50+ real interview questions with detailed answers
✓ Resume optimization for research-focused roles

Best For:
PhDs, researchers, and senior ML engineers with 10-15 hours/week to invest
2. Research Engineer Strategy ($499)
Get a personalized research engineer roadmap in 60 minutes - clarity on your specific path.

What's Inside:
✓ 60-minute 1:1 video call with Dr. Teki
✓ Skills gap assessment across all interview dimensions
✓ Custom 12-week roadmap based on YOUR background
✓ Target company prioritisation and culture fit analysis
✓ CV/LinkedIn repositioning for research roles
✓ Publication and portfolio strategy
✓ Session recording + written action plan
✓ Research Engineer Career Guide included FREE ($79 value)

Best For:
Engineers transitioning from industry or academia who need strategic clarity
3. Research Engineer Sprint ($1599)
Interview-ready in 3 weeks. 3 focused mocks. Expert feedback. 

What You Get:
✓ 3 x 60-minute mock interviews covering all RE interviews: 
   → ML Coding from Scratch (Transformer implementation)
   → ML Debugging (The "Stupid Bugs" round)
   → Research Discussion & Paper Analysis
✓ Written feedback after each session
✓ Company-specific guide (OpenAI, Anthropic, or DeepMind)
✓ 3 weeks async email support
✓ Research Engineer Career Guide included FREE 

Payment:
Pay $1599 upfront or $899 in 2 instalments

Best For:
Candidates with interviews scheduled who need intensive, focused practice
4. Research Engineer Intensive ($3499)
The intensive coaching program with Strategy and Mocks

What You Get:
✓ 3 x 60-minute 1:1 coaching sessions 
✓ 3 x technical mock interviews with expert feedback
✓ Complete math foundations bootcamp 
✓ ML implementation practice with code review
✓ Research paper reading group guidance
✓ CV + LinkedIn + Portfolio transformation
✓ Company-specific prep (OpenAI, Anthropic, DeepMind)
✓ Unlimited async email support for 6 weeks

Payment:
Pay $3499 upfront or $1899 in 2 instalments

Best For:
Candidates with interviews in 6-8 weeks who need both 
Strategic positioning AND Interview practice
5. Research Engineer Accelerator ($5999)
The complete 3 month system: coaching, mocks, 1-1 support. 

What You Get:
✓ 4 x 60-minute 1:1 coaching sessions 
✓ 5 x technical mock interviews with expert feedback
✓ Complete math foundations bootcamp 
✓ ML implementation practice with code review
✓ Research paper reading group guidance
✓ CV + LinkedIn + Portfolio transformation
✓ Company-specific prep (OpenAI, Anthropic, DeepMind)
✓ Unlimited async email support for 12 weeks

Payment:
Pay $5999 upfront or $2199 in 3 instalments

Best For:
Serious candidates who want complete strategic and interview support with a 3 month timeline to hitting the job market
Save with Bundles
Starter Bundle:  
Career Guide ($79) + Strategy ($499) - $499 (save $79)

Interview Ready:  
Strategy ($499) + Interview Sprint ($1599) - $1999 (save $99)
​
→ Book a Call to discuss bundles or custom offerings
6. Frontier AI Lab Specific Research Career Guides
Ready to target a specific frontier AI lab?
These 100+ page intelligence briefs give you insider knowledge of exactly how OpenAI, Anthropic, and DeepMind hire researchers.
OpenAI Research Careers Guide 
The definitive guide to landing research roles at the lab behind GPT-4 and o1.

What's Inside:
✓ OpenAI's research org structure: Safety, Alignment, Capabilities, Multimodal teams
✓ The OpenAI interview loop: what to expect at each stage
✓ Technical deep-dives they actually ask (RLHF, scaling laws, interpretability)
✓ How to position academic research for OpenAI's mission
✓ Compensation benchmarks: RSUs, refreshers, and negotiation leverage
✓ 30+ real interview questions from Research Engineer candidates

Best For:
Researchers targeting OpenAI specifically, especially those interested in alignment, safety, or frontier capabilities research.
Anthropic Research Careers Guide 
The complete playbook for joining the AI Safety pioneers..

What's Inside:
✓ Anthropic's unique research culture and hiring philosophy
✓ Team breakdown: Alignment Science, Interpretability, Policy, Trust & Safety
✓ The Anthropic interview process: research presentations, technical screens, culture fit
✓ How to demonstrate alignment with Anthropic's safety-first mission
✓ What "research taste" means at Anthropic and how to show it
✓ Compensation data and equity structure for research roles 
✓ 30+ interview questions specific to Anthropic RE roles

Best For:
Safety-focused researchers, interpretability specialists, and those who want to work on AI alignment at a frontier lab.
Google DeepMind Research Careers Guide 
Understand the Nobel Prize-winning AI lab, culture, and roles.

What's Inside:
✓ Post-merger org structure: DeepMind Research, Google AI, and how they intersect
✓ Team deep-dives: AlphaFold, Gemini, Robotics, Neuroscience, Safety
✓ The DeepMind interview process: coding, research, and team matching
✓How to leverage publications for DeepMind roles
✓ London vs. US opportunities and visa considerations
✓ Compensation: Google RSUs, DeepMind bonuses, and total comp benchmarks
✓ 30+ real interview questions from DeepMind RE candidates

Best For:
Academic researchers with strong publication records targeting DeepMind's research-heavy culture.
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7. From Coaching to Career Success
My mentees have consistently achieved placements at some of the world's leading technology companies and research institutions including Anthropic, Apple, Google, Meta, Amazon, Microsoft, Salesforce, LinkedIn, Databricks, Twitter. Beyond industry, I also coach candidates interested in academia, with mentees gaining admission to MSc & PhD programs at top institutions like UCL.
Avni, AI Research Engineer, USA:
"I'm so grateful to have found Sundeep as a career coach. He is knowledgeable about the most up-to-date resources for interview preparation and about the types of questions that may be asked. He's also pragmatic in understanding my career goals and helping me with mock interviews. Not only that, he is a kind and compassionate person."

Michael, AI Research Scientist, USA:
"We did multiple mock interviews adapted to the needs of each interview and worked through the very last step which is salary negotiations. It's great to have someone you can trust throughout the stressful period of multiple interviews that can provide great advice and support."

Kensen, Data Scientist, USA:
"Sundeep was a great mentor for preparing me for FAANG-level data scientist interviews. I was able to learn a lot about his frameworks and tips, which were ultimately useful for my actual onsite interviews. Thanks to him, I received an offer from a FAANG company."

Deepti, Data Science Manager, USA:
"If you want the highest ROI for your job hunt efforts, working with Sundeep is where you want to invest your time, financial resources, and effort in. His practice interview approach is thoughtful and deliberate with detailed and constructive feedback."

Margarida, Data Scientist, Spain:
"The coaching sessions with Sundeep are more than excellent. He has an exceptional knowledge of the technical, corporate and strategic aspects related to data science jobs and recruiting processes. Coming himself from academia, he also provides 
exceptional guidance for PhDs willing to start a career as data scientists in the industry."

→ See All Testimonials
8. Your AI Research Engineer Coach
Oxford-Trained Neuroscientist -> Amazon Alexa AI Scientist -> AI Career Coach
Dr. Sundeep Teki brings a unique combination of academic rigor, hands-on AI industry experience, and proven coaching methodology to career transformation.

What Makes Dr. Teki's Approach Different:

Systematic, Proven Frameworks
  • Every recommendation backed by data from 100+ coaching clients
  • Proprietary positioning strategies optimized for RE recruiter searches
  • Portfolio project specifications that consistently generate interviews
  • Interview preparation techniques with high success rate and 5* reviews​

Neuroscience-Backed Methodologies
  • Spaced repetition schedules for interview preparation (maximizes long-term retention)
  • Memory consolidation techniques for technical concepts (3× faster learning)
  • Stress inoculation training for high-pressure scenarios (reduces interview anxiety)
  • Cognitive load management for complex system design discussions

Academic Foundation:
  • PhD in Neuroscience from University College London (UCL)
  • Postdoctoral Research Fellow at University of Oxford
  • 40+ peer-reviewed publications with 3,000+ citations
  • Published in top AI conferences including INTERSPEECH, COLING, AAAI

AI Industry Leadership:
  • Research Scientist at Amazon Alexa AI (Speech & NLP)
  • Led Conversational AI Applied Research at Swiggy
  • Head of AI at Docsumo (25+ ML engineers; Document AI)
  • 17+ years of AI/ML experience across USA, UK, India, France

Ability to Coach for 4 AI roles (read why and how):
Coaching Track Record:
Research Engineer Deep Dive & Analysis:
  • AI Research Engineer Guide - OpenAI, Anthropic and Google Deepmind: Complete interview guide for cracking AI Research Engineer roles at frontier labs. Covers: full process breakdowns for OpenAI (6-8 weeks, coding-heavy), Anthropic (3-4 weeks, 100% CodeSignal accuracy required, safety-focused), DeepMind (<1% acceptance, math quiz rounds); seven question types (Transformer implementation from scratch, ML debugging, distributed training 3D parallelism, AI safety/ethics, research discussions, system design, behavioral STAR); cultural differences (OpenAI = pragmatic scalers, Anthropic = safety-first, DeepMind = academic rigorists)); 12-week prep roadmap (math foundations → implementation → systems → mocks); real questions, debugging scenarios, and offer negotiation.

  • The Complete Guide to Post-Training LLMs: How SFT, RLHF, DPO, and GRPO Shape LLMs: Post-training is now where the majority of a large language model's usable capability is created - not pre-training. This practitioner-oriented deep-dive covers the full three-stage pipeline (SFT, Preference Alignment with DPO/RLHF, and RL with verifiable rewards via GRPO), with technical breakdowns of how each technique works, when to choose one over another, and how OpenAI, Anthropic, and Google DeepMind approach post-training differently. Includes compute cost analysis (QLoRA fine-tuning a 70B model for under $30), compensation benchmarks for post-training specialists ($200K-$450K+ with a 15-25% premium over general ML engineering), a 12-week preparation roadmap, and the interview questions you should expect at each major lab. Essential reading for ML engineers, Research Engineers, and Research Scientists targeting post-training, alignment, or RLHF roles at frontier AI companies in 2026.

  • How to Get Hired at OpenAI, Anthropic, and Google DeepMind in 2026: The definitive guide to landing Research Engineer and Research Scientist roles at the three frontier AI labs with <1% acceptance rates. Covers: OpenAI's unique research discussion round (paper analysis sent in advance), Anthropic's safety assessment that eliminates more strong candidates than technical rounds, and DeepMind's hiring committee process with Googleyness evaluation. Breaks down company-specific technical topics weighted by actual frequency—practical coding vs. LeetCode, CodeSignal thresholds (520+/600), first-principles maths, JAX/TPU preparation. Includes cultural signals that trigger "strong hire" decisions: "AGI focus" and "intense & scrappy" (OpenAI), seven core values and Constitutional AI (Anthropic), "intellectual curiosity" and scientific rigour (DeepMind). Features compensation benchmarks ($500K-$800K+ RS median), equity structures (RSUs, GOOG, retention bonuses up to $1.5M), and 12-week preparation roadmaps. Based on 100+ successful placements at frontier AI labs. [5 min read, senior ML/research-level]

  • How to Improve Deep Learning Skills in 2026 - A Practitioner's Roadmap: Senior deep learning engineers now earn $211K+ on average, with GPU optimization specialists commanding a 30-50% salary premium - yet only 10% of AI/ML projects create positive financial impact, revealing a massive skills gap between model building and production deployment. This practitioner's roadmap covers six skill pillars: mastering foundational mathematics (linear algebra, information theory, KL divergence), going deep on PyTorch (which appears in 42% of ML engineer postings), building transformer fluency from the ground up (RoPE, GQA, SwiGLU), closing the research-to-production gap (quantization, distributed training, vLLM serving), developing domain specialisation, and learning through building in public. Includes specific mental models that accelerate learning (bias-variance lens, gradient flow perspective, information bottleneck), the full production stack from torch.compile to KV-cache optimization, and career context across all four frontier AI roles (Research Scientist, Research Engineer, AI Engineer, FDE). 

→ Start your Research Engineer journey:
9. FAQs
 1. Do I need a PhD to become a Research Engineer?

Not necessarily, but it helps. OpenAI and Anthropic increasingly hire MS/BS candidates with strong engineering track records. DeepMind has a stronger PhD preference. The key is demonstrating "research sensibility" - ability to read papers, implement novel ideas, and think critically about AI. The guide includes specific strategies for both PhD and non-PhD candidates.

2. How is the Research Engineer interview different from standard SWE interview?

Dramatically different. RE interviews include:
  • Math/ML Theory Quiz - oral questions on Linear Algebra, Calculus, Probability
  • ML Coding from Scratch - implementing Transformers, not LeetCode
  • ML Debugging - fixing broken training loops, not runtime errors
  • Research Discussion - presenting and defending your work like a PhD defense
  • AI Safety - especially at Anthropic, this can be a "killer" round
Standard LeetCode prep covers maybe 20% of what you need.

​3. Is the 3-month preparation timeline realistic?

For candidates with strong ML foundations who commit 15-20 hours/week, yes.
  • Weeks 1-4: Math foundations refresh + paper reading
  • Weeks 5-8: ML implementation practice + debugging
  • Weeks 9-10: System design + distributed training
  • Weeks 11-12: Mock interviews + cultural fit
If you're coming from a non-ML background, plan for 4-5 months instead.

​4. Do I need both the Research Engineer Guide and a Company Guide?

They serve different purposes
  • The Research Engineer Guide teaches you how to prepare (12-week roadmap, ML coding, research discussions).
  • The Company Guides tell you what each specific lab looks for and exactly how their interviews work.
  • Most candidates use both.

​5. Which Company Guide should I buy if I'm only targeting one lab?
Buy the guide for your top-choice company.
If you're undecided:
- OpenAI for capabilities/scaling research,
- Anthropic for alignment/interpretability,
DeepMind for fundamental research with academic rigor.

6. What's the difference between Research Engineer and Research Scientist at these labs?
Research Engineers focus on implementing and scaling research ideas.
Research Scientists drive the research agenda and publish.
The Company Guides cover both roles in depth, including how the interview processes differ.

7. How should I prepare for the AI Safety round at Anthropic?

Anthropic's safety round is often the deciding factor. They're looking for:
  • Genuine concern about AI risks (not dismissive, not paralysed by fear)
  • Understanding of RLHF, Constitutional AI, Red Teaming
  • Nuanced positions on alignment and safety
  • "Responsible scaling" mindset
The guide dedicates an entire chapter to AI Safety & Ethics preparation.

Bottom line
If you invest 90 days systematically following the guide and still struggle, that's a signal that coaching would accelerate your progress significantly.
→ Book a Discovery call