How to Get a Better Machine Learning Job After a Lay-Off or Redundancy

Contents

• Understanding Redundancy in Machine Learning

• Step 1: Reset Your Mindset and Reflect on Direction

• Step 2: Define Your ML Specialisms and Tools

• Step 3: Rebuild Your CV and Model Portfolio

• Step 4: Optimise Your LinkedIn, GitHub and Project Visibility

• Step 5: Message Recruiters and Reconnect with Hiring Managers

• Step 6: Apply Selectively and Follow Up

• Step 7: Upskill in High-Impact ML Technologies

• Step 8: Explore Contract, Freelance or Research Roles

• Step 9: Balance Job Hunting with Self-Care

• Bonus: Top UK Employers Hiring for ML Roles in 2025

• Final Thoughts: Redundancy as a Machine Learning Reset

Understanding Redundancy in Machine Learning

ML redundancies can result from AI budget cuts, restructuring, or shifting priorities (e.g. moving from R&D to product delivery). This isn’t a reflection of your talent—just timing.

ML skills are still highly valued, especially in:

• Predictive modelling and optimisation

• Deep learning (vision, NLP)

• Generative AI (LLMs, transformers)

• Recommendation systems

• MLOps, deployment and monitoring

• Real-time inference and embedded ML

Step 1: Reset Your Mindset and Reflect on Direction

Take time to pause and reflect:

• What did you enjoy most in your last role: research, experimentation, deployment, stakeholder interaction?

• Do you want to work in a start-up, scale-up, or established enterprise?

• Are you most motivated by impact, innovation, learning or stability?

This insight helps guide your next move.

Step 2: Define Your ML Specialisms and Tools

Clarify your core strengths:

• Do you focus on classical ML, deep learning, time-series forecasting, NLP, or reinforcement learning?

• What tools are in your stack? (e.g. Python, TensorFlow, PyTorch, Scikit-learn, MLflow, Weights & Biases, Docker, SageMaker)

• Are you familiar with MLOps workflows (CI/CD, model monitoring, cloud deployment)?

Step 3: Rebuild Your CV and Model Portfolio

Your CV should:

• Start with a focused headline and profile summary (e.g. “Machine Learning Engineer | NLP | PyTorch | MLOps”)

• Use bullet points to showcase real impact (e.g. “Improved F1 score by 22% through model re-architecture”)

• List datasets, modelling approaches, evaluation metrics, and tools used

• Include a link to your GitHub, portfolio site, or Hugging Face profile

Step 4: Optimise Your LinkedIn, GitHub and Project Visibility

LinkedIn Tips:

• Headline: “Machine Learning Engineer | Deep Learning | Open to Work”

• About: Briefly highlight projects, strengths, and values

• Add certifications, project write-ups, or talks

GitHub Tips:

• Keep repos tidy, well-documented, and reproducible

• Include projects like end-to-end ML pipelines, model evaluation dashboards, or fine-tuned transformers

• Add notebooks, README summaries, and links to live demos

Sample LinkedIn About Section:

Machine Learning Engineer | NLP & MLOps | Open to Work

I’m a technically curious ML engineer with 4+ years of experience building and deploying machine learning models. After a recent redundancy due to budget cuts, I’m seeking a new role where I can drive real-world impact with scalable AI solutions.

Tech stack: Python, PyTorch, MLflow, FastAPI, Hugging Face, Docker, GCP, GitHub Actions

Let’s connect if you’re hiring or collaborating on applied ML.

Step 5: Message Recruiters and Reconnect with Hiring Managers

Recruiter Message Example:

Subject: Machine Learning Engineer | NLP | Available Immediately

Hi [Recruiter’s Name],

I’m seeking new ML roles following a redundancy and have 4+ years' experience in NLP, transformer models, and model deployment. I’ve attached my CV and GitHub—would love to hear about any relevant openings you’re working on.

Best,
[Your Name]
[LinkedIn]
[GitHub]
[CV attachment]

Hiring Manager Follow-Up Example:

Subject: Application – ML Engineer Role at [Company Name]

Dear [Hiring Manager],

I recently applied for the Machine Learning Engineer role at your company and wanted to express my strong interest. I bring hands-on experience fine-tuning LLMs and deploying scalable models via API endpoints. Following a recent layoff, I’m available immediately and excited to contribute.

CV attached—happy to discuss further.

Kind regards,
[Your Name]

Step 6: Apply Selectively and Follow Up

Avoid mass applications:

• Prioritise jobs that match your domain and tech stack

• Tailor CVs and cover letters with keywords from job specs

• Keep a tracker (company, role, date, follow-up date)

• Revisit jobs and follow up after 7–10 days

Step 7: Upskill in High-Impact ML Technologies

Redundancy is a great time to deepen or diversify:

• Learn about generative models (transformers, diffusion models)

• Explore deployment and monitoring tools (e.g. BentoML, EvidentlyAI)

• Earn certifications (Google ML Engineer, Databricks, DeepLearning.AI)

• Join open-source projects or contribute to Hugging Face spaces

Step 8: Explore Contract, Freelance or Research Roles

Consider:

• Freelance projects via Toptal, Braintrust, or Upwork

• ML consultant or contractor roles

• Research collaborations with universities or think tanks

• Contributing to or launching open-source AI tools

Step 9: Balance Job Hunting with Self-Care

Redundancy can trigger burnout or anxiety. Stay on track by:

• Creating a structured weekly schedule

• Applying for redundancy pay or Universal Credit

• Talking to peers, mentors or industry Slack groups

• Keeping active and taking tech breaks

Bonus: Top UK Employers Hiring for ML Roles in 2025

1. DeepMind (London)

2. Faculty AI

3. Babylon Health

4. Deliveroo

5. Ocado Technology

6. Spotify UK

7. BBC R&D

8. NHS England (AI & ML teams)

9. AstraZeneca

10. Monzo & Starling Bank

11. GSK AI Labs

12. The Alan Turing Institute

13. Stability AI

14. Arm

15. Improbable

Final Thoughts: Redundancy as a Machine Learning Reset

Redundancy can offer unexpected clarity. Use this time to refine your goals, improve your visibility, and reposition your career.

You’re not starting from scratch—you’re re-entering with deeper insight and resilience.

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