Counter Offer Letter Examples: Email Templates & How to Format Them
By
Liz Fujiwara
•
Jan 15, 2026
Picture this: you’re a Staff ML Engineer in San Francisco with a $220,000 base offer for an exciting role, but you know peers are earning closer to $250,000 and the equity feels light. Should you accept, or negotiate?
In today’s AI hiring market, compensation is complex and verbal hints are meaningless without a clear, written counter. This article shows how to write a counter offer email that is specific, professional, and effective, with templates tailored to AI roles like Research Scientist, AI Infra Engineer, and LLM Engineer.
We also explain how platforms like Fonzi, a curated marketplace for AI talent with transparent salary bands and Match Day events, change when and how to negotiate so your next offer reflects your true market value.
Key Takeaways
A counter offer letter is a standard, professional step for AI engineers, ML researchers, and LLM specialists, signaling market awareness and serious interest rather than entitlement.
Strong counter offers are specific and data-driven, referencing concrete technical impact and current market benchmarks, and often cover more than base salary, including equity, bonuses, research time, and compute resources.
Fonzi helps candidates negotiate from a position of strength through transparent compensation bands and Match Day events that create parallel offers and clearer leverage.
What Is a Counter Offer Letter (for AI & ML Roles)?
A counter offer letter is a structured email or document sent after you receive a written job offer. Its purpose is to propose adjustments to salary, equity, remote policy, or other terms before you accept the position.
For AI engineers and ML researchers, counter offers often go beyond base salary. You may need to negotiate research time allocation, model ownership or IP rights, or GPU budget for experiments. For example, a Senior LLM Engineer at a Series C startup in London might accept the proposed salary but counter for a larger equity stake and guaranteed access to compute resources.
Sending a counter offer signals serious interest. You are telling the hiring manager that you want the role, but under conditions that match your market value and career goals. This is not about being difficult. It is about ensuring both sides enter the relationship with aligned expectations.
For technical roles, non-monetary terms can matter as much as cash compensation. A Research Scientist might prioritize protected time for independent research over a modest increase in base pay. An infra engineer might care more about working with modern distributed training systems than maximizing a signing bonus.
When You Should Send a Counter Offer Letter
Not every offer warrants a counter, but in competitive AI markets, employers often budget for negotiation and expect thoughtful responses. Sending a counter offer makes sense when:
Your base salary is 10–20% below current market rates. If Staff ML Engineers in your city earn around $240,000 and your offer is $200,000, market data from sources like levels.fyi or Glassdoor can support a counter.
Equity is meaningfully below comparable AI startups. A 0.15% grant at a Series B company may fall short if similar roles offer 0.3–0.5%.
Research time or computer access is unclear. Ambiguity around publication freedom, conference support, or GPU allocation is best addressed upfront.
Remote flexibility does not match expectations. On-site requirements that differ from what was discussed are a reasonable negotiation point.
Professional development is missing. Conference travel and continuing education are important for many AI roles and worth addressing before acceptance.
A typical timeline looks like this: you receive a written offer on Tuesday afternoon, spend Wednesday researching the market and clarifying priorities, and send a thoughtful counter offer on Thursday. A 24–48 hour turnaround shows care without stalling.
In AI hubs like the SF Bay Area, London, Toronto, and Berlin, negotiation is expected. A polite, well-supported counter offer rarely hurts your chances. What often does is accepting immediately, which can signal that you may have left value on the table.
Avoid countering before you have a written offer. Verbal discussions about “around $200k” aren’t concrete enough to negotiate against. Similarly, never counter after you’ve already signed the job offer letter. At that point, you’ve accepted the terms.
Key Elements to Include in a Counter Offer Letter
Every effective counter offer letter follows a clear structure. Think of it like a well-organized design doc: it should be easy to scan, grounded in evidence, and lead to a clear ask.
Here are the core components:
Professional salutation with the hiring manager’s name
Gracious introduction expressing genuine enthusiasm
Clear description of the original offer terms
Specific counter terms with exact numbers
Justification backed by market data and your achievements
Positive closing that invites further discussion
Let’s break down each element with examples tailored to roles like Applied Scientist, NLP or ML Platform Engineer.
A Professional Salutation
Always use the hiring manager or recruiter’s name. Generic greetings signal that you didn’t bother to personalize your message.
For a Head of ML Research: “Dear Dr. Chen,”
For a startup CTO you’ve been emailing casually: “Hi Alex,”
For a formal enterprise role: “Dear Ms. Thompson,”
Match the level of formality to the company culture. A FAANG-adjacent research lab expects more formal communication than a seed-stage AI startup where everyone uses first names in Slack. When in doubt, mirror the tone they’ve used with you throughout the interview process.
A Gracious Introduction
Your first paragraph should thank them for the offer and express genuine excitement about specific aspects of the role.
Start by mentioning the exact job title and proposed start date:
“Thank you for offering me the Senior LLM Engineer position with a proposed start date of 1 August 2026.”
Then add 1–2 sentences about what excites you. Be concrete and mention specific projects, technologies, or challenges that appeal to you rather than using vague phrases like “great opportunity.”
“I’m excited about the opportunity to work on your real-time recommendation systems at global scale, and I was particularly impressed by the infrastructure challenges your team described during our conversations.”
This shows you’ve paid attention and genuinely want the role. It sets a collaborative tone before you make any requests.
Stating Your Counter Offer
This is where you get specific. No hedging, no vague language. State exactly what you’re proposing.
“Based on my research into current compensation for Staff ML Engineers in the Bay Area and my current total compensation, I would be comfortable accepting this role at a base salary of $245,000, up from the proposed $220,000.”
For AI researchers, this section might include:
Proposed new base salary (from $190,000 to $215,000)
Adjusted equity amount (from 0.3% to 0.5% over four years)
Signing bonus request ($25,000)
Research time allocation (20% protected for independent projects)
Compute budget (dedicated GPU quota per quarter)
If you’re combining multiple asks, prioritize them by leading with your most important request, usually base salary, and follow with secondary items like equity or benefits.
Justifying Your Request
Ground your request in three sources: market data, your impact history, and competing opportunities if applicable.
Market data: Reference specific ranges.
“According to levels.fyi 2025 data, Staff ML Engineers at similar-stage companies in New York earn between $230,000 and $260,000 in base salary.”
Your achievements: Mention concrete results.
“In my current role, I led the team that shipped a retrieval-augmented generation system that reduced support ticket volume by 35%. I also optimized our inference pipeline, cutting p95 latency from 250ms to 90ms.”
Competing offers: If relevant, mention them factually.
“I’m also in late-stage conversations with another company in this space, which has informed my understanding of current market rates.”
For a Research Scientist, this might sound like:
“My publications at NeurIPS 2024 and ICML 2025 demonstrate my ability to produce research that advances the field, and I want to ensure I can continue this trajectory in my next role.”
For an Infra Engineer:
“Over the past three years, I’ve designed and deployed distributed training systems handling 500+ GPUs, directly enabling models that now serve 10 million users daily.”
Keep this section to 4–6 sentences. It should read like a succinct performance summary, not a lengthy self-promotion.
A Positive, Forward-Looking Closing
End with appreciation, reaffirm your interest, and invite conversation.
“I’d be happy to discuss these details this week if helpful. I’m confident we can find a structure that works well for both of us.”
Add a gentle time reference without sounding like an ultimatum:
“If possible, I’d love to connect before Friday, 17 April, to keep the timeline on track.”
Other effective closings:
“Thank you again for this exciting opportunity. I look forward to hearing your thoughts and am flexible on how we move forward.”
“I remain very enthusiastic about joining the team and would like to discuss this at your earliest convenience.”
Counter Offer Email Templates for AI & ML Roles
Below are four fully written email templates you can adapt for your situation. Each models a respectful tone, direct ask, and clear justification anchored in industry data and concrete experience.
Template 1: Requesting Higher Base Salary (Senior ML Engineer)
Subject: Offer for Senior ML Engineer – Compensation Discussion
Dear Sarah,
Thank you for extending the offer for the Senior ML Engineer position at [Company Name]. I’m genuinely excited about the opportunity to own the ranking models and scale the training pipelines your team described during our conversations. The technical challenges here align perfectly with where I want to focus my career.
After careful consideration and reviewing current market data, I would like to discuss the base salary component. The offer of $200,000 is below what I’m seeing for similar Senior ML Engineer roles in New York based on levels.fyi 2025 data, which shows a range of $215,000 to $250,000 for comparable positions.
Given my five years of experience building production ML systems, including the recommendation engine at my current company that improved conversion by 18%, I believe a base salary of $225,000 would better reflect both market rates and the value I will bring to your team.
I’m very much looking forward to joining and would be happy to discuss this further at your convenience.
Best regards,
[Your Name]
Template 2: Requesting Better Equity & Bonus Mix (LLM Engineer at Series B Startup)
Subject: Offer Discussion – Equity and Compensation Structure
Hi Marcus,
Thank you for the offer to join [Company Name] as an LLM Engineer. I’m excited about the chance to build the next generation of your conversational AI products, and the team’s approach to responsible deployment really resonated with me.
The proposed base salary of $210,000 works well for me. However, I’d like to discuss the equity component. The current offer of 0.15% over four years is below what I’ve seen at comparable Series B companies for this level, where grants typically range from 0.3% to 0.4%.
I believe strongly in the company’s long-term trajectory and want to be meaningfully aligned through ownership. Based on my experience shipping LLM-powered features that drove 40% growth in user engagement at my previous startup, I’d like to propose an equity grant of 0.35% along with a performance-based bonus structure tied to product milestones.
I’m open to discussing alternatives if this structure presents challenges. Looking forward to finding a mutually beneficial agreement.
Best,
[Your Name]
Template 3: Requesting Research Time, Compute, or Conference Support (Research Scientist)
Subject: Offer for Research Scientist – Discussion on Research Terms
Dear Dr. Müller,
Thank you for offering me the Research Scientist position at [Company Name] in Zurich. I’m thrilled about the opportunity to contribute to your work on multimodal learning, and the caliber of research coming from your team has been impressive.
While the salary offer meets my expectations, I’d like to clarify a few terms that will directly impact my ability to contribute at the highest level:
First, I’d like to propose 20% protected research time for independent exploration and publication, similar to what I’ve had in my current role where it led to two accepted papers at NeurIPS 2024.
Second, I’d appreciate a defined quarterly GPU budget for experiments, around 1,000 A100 hours per quarter, to enable scaling experiments that produce publishable results. Third, I’d like to confirm travel support for at least two major conferences per year, such as NeurIPS and ICML, for presentation and networking. These aren’t perks; they’re enablers for the kind of impact I want to deliver, and clarifying these expectations now will help avoid friction after onboarding. I’m happy to discuss this further whenever it works for you.
Warm regards,
[Your Name]
Template 4: Responding to a Lowball Offer (AI Infra Engineer)
Subject: Offer for AI Infra Engineer – Compensation Discussion
Dear Ms. Patel,
Thank you for the offer to join [Company Name] as an AI Infra Engineer. I’m genuinely excited about the technical challenges your team is tackling; distributed training at your scale and the model serving architecture you described are exactly the problems I want to solve. I want to be direct about the compensation. The proposed base salary of $175,000 is approximately 15% below both my current compensation and what I’m seeing for comparable AI Infra roles in Toronto based on Glassdoor 2026 data and discussions with peers. Given my track record, including designing the distributed training system at my current company that reduced training time by 60% and now supports models serving 8 million daily users, I believe a base salary in the range of $200,000 to $210,000 would be more aligned with industry standards and the impact I expect to deliver.
Is there room to move closer to this range? I’m committed to finding a way forward because I believe this role is the right fit for both of us.
Thank you for your further consideration.
Best regards,
[Your Name]
How AI Is Changing Hiring—and Where Fonzi Fits In
In 2026, many companies will use AI to screen resumes, summarize interviews, and prioritize candidates for AI-related roles. While this can speed up the hiring process, it creates new challenges for job candidates. The risks are real: opaque filtering systems that reject qualified candidates before a human sees their resume, bias amplification when models over-weight certain keywords or credentials, and candidates left wondering why they never heard back. Fonzi takes a different approach. As a marketplace specifically for AI and ML talent, Fonzi uses AI to create clarity, not confusion. Structured profiles, transparent preferences, and matched opportunities mean you know what you’re getting into before interviews begin. Companies on Fonzi commit to responsible AI usage in hiring, including human-in-the-loop review for key decisions, documented fairness checks, and no fully automated rejections for core hiring outcomes.
How Companies Use AI in the Hiring Process Today
Here’s what’s actually happening behind the scenes when you apply for AI roles in 2026:
Automated resume parsing ranks candidates for LLM engineer roles based on keyword matches and inferred skills. A large bank hiring ML Ops engineers might use these systems to reduce 500 applications to 50 before a recruiter sees anything.
Interview intelligence tools summarize coding rounds and behavioral interviews, flagging candidates for follow-up or rejection based on transcribed responses.
Model-based matching connects candidates to internal requisitions across multiple teams, sometimes routing applications without the candidate knowing which roles they’re being considered for.
The pitfalls are significant. These systems can over-weight keyword matches, penalizing candidates with non-standard career paths. They might misinterpret research-heavy CVs that don’t fit the expected format. They also create asymmetric information: companies know exactly how they evaluated you, but you have no idea why you were passed over.
Understanding this landscape helps you know when to insist on written offers and use counter offer letters to correct initial mispricing that algorithms may have introduced.
How Fonzi Uses AI to Create Clarity, Not Confusion
Fonzi’s matching engine uses structured signals such as skills, publications, infrastructure experience, and preferred tech stack to connect AI talent with roles, while humans make the final match decisions.
Here’s what that means for you:
Detailed role information upfront. Before you interview, you see salary bands, equity ranges, and expectations around research versus product work. No more discovering at the offer stage that the compensation is 30% below your target.
Preference-based matching. You set your preferences: remote, hybrid, or on-site; research-focused or infra-focused; early-stage startup or established company. The system respects these constraints.
Transparent timelines. You know when to expect interview invites, feedback, and offers. The interview process moves at a predictable pace.
Fonzi does not auto-reject candidates purely via model outputs. There is always a human recruiter or hiring partner reviewing outcomes before decisions are made.
Reducing Bias and Protecting Candidate Experience on Fonzi
Fonzi masks certain demographic details at early stages to focus on what matters: skills, projects, and outcomes for AI and infra roles.
Companies on Fonzi agree to SLA-like response times so you won’t wait weeks wondering whether you’ll receive an offer, which matters because delayed responses weaken your negotiating position and increase pressure to accept whatever is offered.
Fonzi also prompts companies to share clear written offers with band information. This makes it easier to write a counter offer letter that references concrete numbers rather than guessing at ranges.
Most importantly, you own your data. You control which companies see your profile and when you’re “live” for new roles. No one accesses your information without your explicit permission.
Understanding Fonzi Match Day: Negotiation with Better Signals
Match Day is a recurring event, typically twice per month, where curated AI candidates are introduced to multiple pre-vetted companies at once, creating natural leverage for negotiation because candidates often receive multiple interview requests within 48 hours and have real data from real companies.
Match Day leads to higher-signal interviews. Companies already align on compensation ranges and role expectations before speaking with candidates. This reduces the chance of wasted interviews where expectations are wildly misaligned.
Step-by-Step: How Match Day Works for Candidates
The sequence works like this:
Profile review. You create or update your Fonzi profile with detailed signals: frameworks you’ve used, scaling experience, publication history, and preferred work arrangements.
Alignment call. A Fonzi talent partner reviews your profile and discusses your goals, target compensation, and role preferences.
Match Day activation. On the designated date, your profile is surfaced to participating companies looking for candidates with your skill set.
Company intros. Within 72 hours, you receive interview invites from companies that want to proceed.
Interviews and offers. You move through compressed interview loops and receive offers, often within a 2-week window.
Why Match Day Leads to Stronger Counter Offers
Multiple offers received in the same 1–2 week period make benchmarking dramatically easier. You can accurately and honestly reference “other opportunities I’m considering” in your counter offer without bluffing.
This is not gamesmanship; it is information symmetry. Companies always know what other candidates they are evaluating. Match Day gives you similar visibility into your own market position.
Fonzi talent partners can help you sanity-check your counter ask ranges before you hit send. If you are wondering whether $240,000 is reasonable or aspirational, someone who has seen dozens of similar negotiations can give you a reality check.
How to Format a Professional Counter Offer Letter or Email
Getting the format right matters almost as much as getting the content right. A rambling, poorly structured counter offer undermines your professionalism, even if your request is reasonable.
The ideal length is 200–400 words. Clear paragraphs, no walls of text. Busy hiring managers appreciate brevity.
For most AI roles, a concise email body works best. You can attach a more formal physical letter as a PDF for enterprise environments, but the email itself should stand alone.
Subject line examples:
For an in-thread reply: “Re: Offer for Senior ML Engineer – Compensation Discussion”
For a new email chain: “Offer Discussion – [Your Name] – AI Infra Engineer”
Recommended paragraph order:
Thank and express enthusiasm (2–3 sentences)
Reference the specific offer terms (1–2 sentences)
State your counter with exact numbers (2–3 sentences)
Justify with market data and achievements (3–4 sentences)
Close with next steps and appreciation (2 sentences)
Always include specific dates when relevant: “the offer dated 12 April 2026” or “proposed start date of 1 June 2026.”
Email vs. Formal Letter: Which Should You Use?
Email is standard for most modern AI teams. Startups, scale-ups, and even large tech companies expect email communication. It is fast, easy to reference, and matches how the rest of your interview process likely occurred.
Use a more formal letter (PDF attachment) in these cases:
European research institutes with traditional academic cultures
Established bank or insurance AI labs with formal HR processes
Government research positions where documentation standards are higher
Even when sending a formal letter, your email body should contain a short summary: “Please find attached my response to the offer for the Research Scientist position. I am excited about the role and would like to discuss a few terms. Looking forward to your thoughts.”
Rule of thumb: match the formality of the offer you received. If they sent a formal offer letter on company letterhead, respond in kind. If the offer came via a friendly email from the hiring manager, a well-structured email reply is appropriate.
Timing, Strategy, and Tone: Maximizing Your Chances
Knowing what to say is only half the equation. You also need to know when to send it, how much to ask for, and how to sound assertive without being adversarial.
Send your counter within 24–72 hours of receiving the written job offer. This shows you’ve given it careful consideration without dragging things out. If the company has given you a deadline, make sure you respond at least a day before it expires.
For reasonable counter ranges in AI roles in 2026: 10–15% on base salary is standard for most situations. Higher asks (15–25%) require strong justification, a lowball offer, exceptional competing offers, or rare skill combinations.
Doing Your Market Research
Before you write a counter offer, you need data. Consult concrete sources:
levels.fyi for 2025–2026 AI role compensation by company and level
Blind salary threads for anonymous peer data
Glassdoor for range estimates and reported offers
Fonzi’s aggregated band insights when using the platform
Compare not just job titles but responsibilities and levels. Titles vary wildly across AI companies.
Create a simple benchmark sheet with 3–5 comparable offers or public ranges. This becomes your evidence base when drafting the letter.
Avoid extreme outlier requests unless strongly justified. Asking for 50% more than the offer with no supporting data will end the conversation before it starts.
Choosing the Right Tone
The ideal tone is confident, collaborative, and data-backed. Think of it like a design review: you’re presenting a reasoned proposal, not issuing demands.
Overly aggressive: “Your offer is insulting given my experience. I expect at least $260,000.”
Better: “Based on my experience and current market data, I believe a base salary of $245,000 would be more aligned with industry standards.”
Overly apologetic: “I’m really sorry to bring this up, and I feel bad asking, but is there any way you might possibly consider a slight increase?”
Better: “I’d like to discuss adjusting the base salary to better reflect my qualifications and current market rates.”
When referencing competing offers, keep it factual: “I’m also in discussions with another company, which has informed my understanding of current compensation ranges.” Don’t name competitors unless asked, and never exaggerate.
Being Open to Negotiation and Alternatives
Decide in advance what you care about most. Rank your priorities:
Base salary
Research time / compute access
Remote flexibility
Signing bonus
Title
This helps you know where to flex. If they cannot move on salary, maybe they can add a larger sign-on bonus or accelerate your first equity refresh.
Suggest alternatives in your letter: “If adjusting the base salary presents challenges, I would be glad to explore changes to the equity grant or signing bonus instead.”
For AI roles, non-salary items can materially impact your career. Protected research time means publications that build your reputation. Conference budgets mean networking that leads to future opportunities.
State your openness explicitly. It signals that you want to find a path forward, not win a confrontation.
Preparing for Interviews and Offers with Fonzi
The best time to prepare for a counter offer is before interviews even begin. How you position yourself throughout the process affects the offers you receive.
Fonzi profiles collect detailed signals such as frameworks, scaling experience, and publication history that help surface better-matched roles. When companies already understand your value before the first call, initial offers tend to be closer to fair market rate.
Fonzi talent partners can review your CV, GitHub profile, and portfolio projects to help frame a compelling value story. This preparation pays dividends when it’s time to write a counter offer letter and you need to articulate your significant value clearly.
You don’t have to navigate the AI hiring market alone.
Showcasing Your Skills for High-Signal Offers
Highlight concrete outcomes in your profile and interviews:
Decreased training costs by 40% through mixed-precision optimization
Improved model quality metrics (BLEU score from 0.31 to 0.38 on internal benchmarks)
Deployed RAG systems serving 5 million queries daily
Built RLHF pipelines that reduced human annotation needs by 60%
Use quantifiable metrics wherever possible: “reduced p95 latency from 250ms to 90ms” or “increased recommendation CTR by 11%.”
These achievements should appear in your Fonzi profile, your resume, and how you discuss compensation expectations. When the hiring manager has already seen your track record, the counter offer letter just reinforces what they already know.
Better storytelling translates to stronger initial offers. Candidates who clearly articulate their impact often receive offers that require less negotiation to reach fair value.
Interview Strategy for AI, ML, and Infra Roles
Focus on signal-rich preparation:
System design for ML infra: distributed training architectures, model serving at scale, feature stores
Deep dives for LLM roles: transformer architectures, attention mechanisms, RLHF, prompt engineering patterns
Coding practice: leetcode-style problems where appropriate, but focus on the style of problems your target company uses
Ask calibration questions during interviews, not about compensation directly, but about level expectations and what success looks like in the first year. Understanding how the company values the role helps you craft a counter offer that lands. Fonzi can share typical interview flows and compensation patterns for specific employers before you enter the loop. Knowing that a company tends to offer at the low end of their band with expectation of a counter changes how you approach the final negotiation.
Conclusion
A counter offer letter is now a standard professional step for AI engineers, researchers, and infra specialists, showing market awareness rather than risk. The strongest letters are specific, data-driven, and clear about priorities such as salary, equity, research time, or remote flexibility while maintaining a professional tone. Fonzi’s AI-powered human-centered marketplace provides transparent compensation data and multiple offers from Match Day, giving you the information needed to negotiate confidently and ensure your next offer reflects your true value.
